100 million poor households by 2005the World Bank has been quick topoint out helping 100 million housemillion poor people could benefit Increasing activity in the United Statescan be expected as ID: 301134
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Vol. XXXVII Journal of Economic Literature, Vol. XXXVII December 1999 Introductionlive in households with per capita in-policymakers and practitioners who havebeen trying to improve the lives of thatbureaucratic sprawl and unchecked cor-ruption abound. And many now believethat government assistance to the pooroften creates dependency and disincen-tives that make matters worse, not bet-ter. Moreover, despite decades of aid,communities and families appear to beincreasingly fractured, offering a fragileAmid the dispiriting news, excite-ment is building about a set of unusualfinancial institutions prospering in dis-tant corners of the worldespeciallyBolivia, Bangladesh, and Indonesia. Thehope is that much poverty can be allevi-atedand that economic and socialmentallyby providing financial ser-vices to low-income households. Theseinstitutions, united under the banner ofmicrofinance, share a commitment toserving clients that have been excludedfrom the formal banking sector. Almostall of the borrowers do so to financeself-employment activities, and manystart by taking loans as small as $75, re-a few programs require borrowers toput up collateral, enabling would-be en-positions as poorly paid wage laborersSome of the programs serve just ahandful of borrowers while others servemillions. In the past two decades, a di-verse assortment of new programs hasbeen set up in Africa, Asia, Latin Amer-ica, Canada, and roughly 300 U.S. sites 1997). Globally, there are nowabout 8 to 10 million households servedby microfinance programs, and some Princeton University. JMorduch@Princeton.Edu. I have benefited from comments fromHarold Alderman, Anne Case, Jonathan Conning,Peter Fidler, Karla Hoff, Margaret Madajewicz,John Pencavel, Mark Schreiner, Jay Rosengard,J.D. von Pischke, and three anonymous referees. Ihave also benefited from discussions with AbhijitBanerjee, David Cutler, Don Johnston, AlbertPark, Mark Pitt, Marguerite Robinson, ScottRozelle, Michael Woolcock, and seminar partici-pants at Brown University, HIID, and the OhioState University. Aimee Chin and Milissa Day pro-vided excellent research assistance. Part of the re-search was funded by the Harvard Institute forInternational Development, and I appreciate thesupport of Jeffrey Sachs and David Bloom. I alsoappreciate the hospitality of the Bank Rakyat In-donesia in Jakarta in August 1996 and of Grameen,BRAC, and ASA staff in Bangladesh in the sum-mer of 1997. The paper was largely completedduring a year as a National Fellow at the HooverInstitution, Stanford University. The revisionArthur Foundation. An earlier version of the pa-per was circulated under the title The Microfi-only. 100 million poor households by 2005.the World Bank, has been quick topoint out, helping 100 million house-million poor people could benefit. In-creasing activity in the United Statescan be expected as banks turn to mi-crofinance encouraged by new teethadded to the Community Reinvestmentlike group-lending contracts and newattitudes about subsidies as the keys totracts effectively make a borrowersneighbors co-signers to loans, mitigat-ing problems created by informationalasymmetries between lender and bor-rower. Neighbors now have incentivesto monitor each other and to excluderisky borrowers from participation, pro-moting repayments even in the absencenomic theorists, and they have broughtglobal recognition to the group-lendingMicrofinance appears to offer a win-win solution, where both financial in-stitutions and poor clients profit. Thefirst installment of a recent five-part se-San Francisco Examiner, example, begins with stories about fourwomen helped by microfinance: a tex-tile distributor in Ahmedabad, India; astreet vendor in Cairo, Egypt; an artistin Albuquerque, New Mexico; and afurniture maker in Northern California.From ancient slums and impoverished vil-lages in the developing world to the tired in-ner cities and frayed suburbs of Americaseconomic fringes, these and millions of otherwomen are all part of a revolution. Somemight call it a capitalist revolution . . . Aslittle as $25 or $50 in the developing world,perhaps $500 or $5000 in the United States,these microloans make huge differences inpeoples lives . . . Many Third World bank-ers are finding that lending to the poor is notjust a good thing to do but is also profitable.bottom-up aspects, attention to com-munity, focus on women, and, most im-portantly, the aim to help the under-served. It is no coincidence that the riseof microfinance parallels the rise of non-governmental organizations (NGOs) inpolicy circles and the newfound attentionto social capital by academics (e.g.,Robert Putnam 1993). Those who leaning poverty while providing incentivesto work, the nongovernmental leadership,the use of mechanisms disciplined byThere are good reasons for excite-ment about the promise of microfi-nance, especially given the politicalcontext, but there are also good reasonsfor caution. Alleviating poverty throughbanking is an old idea with a checkeredpast. Poverty alleviation through theterpiece of many countries develop-through the 1980s, but these experi-ences were nearly all disasters. Loan re-payment rates often dropped well belowand much credit was diverted to the po-litically powerful, away from the in-tended recipients (Dale Adams, Douglas Recent theoretical studies of microfinance in-clude Joseph Stiglitz 1990; Hal Varian 1990; Timo-thy Besley and Stephen Coate 1995; AbhijitMaitreesh Ghatak 1998; Mansoora Rashid andRobert Townsend 1993; Beatriz Armendariz deAghion and Morduch 1998; Armendariz and Chris-tian Gollier 1997; Margaret Madajewicz 1998;Aliou Diagne 1998; Bruce Wydick 1999; JonathanConning 1997; Edward S. Prescott 1997; and Loïc1570 What is new? Although very few pro-programs report loan repayment ratespercent. The programs have also provenable to reach poor individuals, particu-larly women, that have been difficult toNowhere is this more striking than inally conservative and male-dominated.The programs there together serveclose to five million borrowers, the vastmajority of whom are women, and, inaddition to providing loans, some of theprograms also offer education on healthissues, gender roles, and legal rights.The new programs also break from thepast by eschewing heavy government in-volvement and by paying close attentionto the incentives that drive efficientBut things are happening fastandgetting much faster. In 1997, a highprofile consortium of policymakers,charitable foundations, and practitionersstarted a drive to raise over $20 billionfor microfinance start-ups in the next tenMicrocredit Summit Report 1997Most of those funds are being mobi-lized and channeled to new, untestedinstitutions, and existing resources arebeing reallocated from traditional pov-nance. With donor funding pouring in,practitioners have limited incentives tostep back and question exactly how andof microfinance is so far unmet, and theboldest claims do not withstand closescrutiny. High repayment rates haveseldom translated into profits as adver-tised. As Section 4 shows, most pro-grams continue to be subsidized di-rectly through grants and indirectlythrough soft terms on loans from do-nors. Moreover, the programs that arebreaking even financially are not thosecelebrated for serving the poorest cli-ents. A recent survey shows that evenmitment to achieving financial sustain-ability cover only about 70 percent oftheir full costs (1998). While many hope that weak fi-nancial performances will improve overtime, even established poverty-focusedprograms like the Grameen Bank wouldhave trouble making ends meet withoutThe continuing dependence on subsi-dies has given donors a strong voice,but, ironically, they have used it topushed donors to argue that subsidiza-tion should be used only to cover start-up costs. But if money spent to supporttives in ways not possible through alter-native programs like workfare or directfood aid, why not continue subsidizingmicrofinance? Would the world be bet-ter off if programs like the GrameenAnswering the questions requiresstudies of social impacts and informa-tion on client profiles by income andoccupation. Those arguing from theanti-subsidy (win-win) position havedata, however. One defense is that, as-suming that the win-win position iscorrect (i.e., that raising real interestrates to levels approaching 40 percentthe depth of outreach), financial viabil-ity should be sufficient to show socialimpact. But the assertion is strong, andthe broader argument packs little punchPoverty-focused programs counter Morduch: The Microfinance Promise likely undermine social objectives, butby the same token there is not yet di-rect evidence on this either. Anecdotesabound about dramatic social and eco-nomic impacts, but there have been fewimpact evaluations with carefully cho-sen treatment and control groups (orwith control groups of any sort), andthose that exist yield a mixed picture ofimpacts. Nor has there been much solidcredit demand to the interest rate, noron the extent to which subsidized pro-grams generate externalities for non-borrowers. Part of the problem is thatdistracting, and results threaten to un-dermine the rhetorical strength of thesupport to those wary of the anti-sub-sidy argument. Without better data, av-erage loan size is typically used to proxyfor poverty levels (under the assump-tion that only poorer households will bewilling to take the smallest loans). Thetypical borrower from financially self-sufficient programs has a loan balanceof around $430with loan sizes oftenmuch higher (1998). In low-income countries, bor-rowers at that level tend to be amongabove the poverty line. Expanding fi-nancial services in this way can fosterValerie Bencivenga and Bruce D. Smith(1991)but it will do little directly toaffect the vast majority of poor house-holds. In contrast, Section 4.1 showsthat the typical client from (subsidized)leviation has a loan balance close to justImportant next steps are being takenby practitioners and researchers whoare moving beyond the terms of earlytopher Dunford, and Warner Wood-worth 1999). The promise of microfi-nance was founded on innovation: newmanagement structures, new contracts,and new attitudes. The leading pro-grams came about by trial and error.Once the mechanisms worked reason-ably well, standardization and replica-tion became top priorities, with contin-ued innovation only around the edges.mally designed nor necessarily offeringthe most desirable financial products.While the group-lending contract is themost celebrated innovation in microfi-nance, all programs use a variety ofimportant, especially various forms ofdynamic incentives and repaymentschedules. In this sense, economic the-also ahead of the evidence. A portion ofdonor money would be well spent quan-tifying the roles of these overlappingmechanisms and supporting efforts todetermine less expensive combinationsof mechanisms to serve poor clients invarying contexts. New managementture of Bangladeshs Association for So-cial Advancement, may allow sharp cost-cutting. New products, like the flexiblesavings plan of Bangladeshs SafeSave,may provide an alternative route to fi-crofinance is that mechanisms matter:the full promise of microfinance canonly be realized by returning to theearly commitments to experimentation,The next section describes leadingprograms. Section 3 considers theoret-ical perspectives. Section 4 turns to1572 financial sustainability, and Section 5takes up issues surrounding the costs andbenefits of subsidization. Section 6 de-scribes econometric evaluations of im-pacts, and Section 7 turns from creditto saving. The final section concludeswith consideration of microfinancein the broader context of economic New ApproachesReceived wisdom has long been thatlending to poor households is doomedput up as collateral. Not long ago, thenorm was heavily subsidized credit pro-vided by government banks with repay-ment rates of 70 80 percent at best. Into poor households by traditional banks1980. By 1988 89, a year of bad flood-18.8 percent (M. A. Khalily and RichardMeyer 1993). Similarly, by 1986 repay-ment rates sank to 41 percent for subsi-dized credit delivered as part of Indiasment Program (Robert Pulley 1989).These programs offered heavily subsi-dized credit on the premise that poorhouseholds cannot afford to borrow atBut the costs quickly mounted andthe programs soon bogged down gov-ernment budgets, giving little incentivefor banks to expand. Moreover, manybank managers were forced to reduceinterest rates on deposits in order tocompensate for the low rates on loans.In equilibrium, little in the way of sav-ings was collected, little credit was de-livered, and default rates accelerated asbanks would not last long. The repeatedfailures appeared to confirm suspicionsthat poor households are neither credit-worthy nor able to save much. More-over, subsidized credit was often di-verted to politically-favored non-poor1992). Despite good intentions, manyprograms proved costly and did little toThe experience of Bangladeshs Gra-meen Bank turned this around, and nowa broad range of financial institutionsoffer alternative microfinance modelsgroups. Other pioneers described belowinclude BancoSol of Bolivia, the Bankstarted by the Foundation for Interna-tional Community Assistance (FINCA).The programs below were chosen withan eye to illustrating the diversity ofmechanisms in use, and Table 1 high-lights particular mechanisms. The func- The Grameen Bank, Bangladeshnot come down from the academy, norfrom ideas that started in high-incomecountries and then spread broadly. Sections 4.1 and 5.1 describe summary statis-tics on a broad variety of programs. See also MariaOtero and Elisabeth Rhyne (1994); (1998); Ernst Brugger and Sarath Rajapa-tirana (1995); David Hulme and Paul Mosley(1996); and Elaine Edgcomb, Joyce Klein, and Part of the inspiration came from observingcredit cooperatives in Bangladesh, and, interest-ingly, these had European roots. The late nine-teenth century in Europe saw the blossoming ofhouseholds save and get credit. The cooperativesstarted by Frederick Raiffeisen grew to serve 1.4million in Germany by 1910, with replications inIreland and northern Italy (Guinnane 1994 and1997; Aidan Hollis and Arthur Sweetman 1997). Inthe 1880s the government of Madras in South In-dia, then under British rule, looked to the Germanexperiences for solutions in addressing poverty in Morduch: The Microfinance Promise Programs that have been set up incago, Boston, and Washington, D.C.cite Grameen as an inspiration. In addi-tion, Grameens group lending modelhas been replicated in Bolivia, Chile,China, Ethiopia, Honduras, India, Ma-laysia, Mali, the Philippines, Sri Lanka,India. By 1912, over four hundred thousand poorIndians belonged to the new credit cooperatives,and by 1946 membership exceeded 9 million (R.Bedi 1992, cited in Michael Woolcock 1998). Thecooperatives took hold in the State of Bengal, theeastern part of which became East Pakistan at in-dependence in 1947 and is now Bangladesh. Inthe early 1900s, the credit cooperatives of Bengalwere so well-known that Edward Filene, the Bos- ton merchant whose department stores still bearhis name, spent time in India, learning about thecooperatives in order to later set up similar pro-(Shelly Tenenbaum 1993). The credit cooperativeseventually lost steam in Bangladesh, but the no-tion of group-lending had established itself and,after experimentation and modification, became TABLE 1HARACTERISTICS Rakyat Badan Village banksMembership2.4 million81,50316 million765,58689,986Average loan balance$134$909$1007$71$191Typical loan term1 year4 123 243 months4 monthsmonthsmonthsPercent female members95%61%23%95%Mostly rural? Urban?ruralurbanmostlyruralmostlyruralruralGroup-lending contracts?yesyesnononoCollateral required?nonoyesnono emphasized?noyesyesnoyesProgressive lending?yesyesyesyesyes schedulesweeklyflexibleflexibleflexibleweeklyTarget clients for lendingpoorlargelynon-poorpoorpoornon-poor sustainable?noyesyesyesnoNominal interest rate on 20%47.5 32 43%55%36 48% loans (per year)50.5% inflation, 19962.7%12.4%8.0%8.0% Grameen Bank: through August 1998, www.grameen.com; loan size is from December 1996, calculatedby author. BancoSol: through December 1998, from Jean Steege, ACCION International, personal communica-tion. Interest rates include commission and are for loans denominated in bolivianos; base rates on dollar loansare 25 31%. BRI and BKD: through December 1994 (BKD) and December 1996 (BRI), from BRI annual dataand Don Johnston, personal communication. BRI interest rates are effective rates. FINCA: through July 1998,www.villagebanking.org. Inflation rate: World Bank World Development Indicators 1574 Tanzania, Thailand, the U.S., and Viet-nam. When Bill Clinton was still gover-nor, it was Muhammad Yunus, founderof the Grameen Bank (and a Vander-bilt-trained economist), who was calledin Arkansas, one of the early microfi-nance organizations in the U.S. AsBangladesh had a terrible famine in 1974. Iwas teaching economics in a Bangladesh uni-versity at that time. You can guess how diffi-cult it is to teach the elegant theories of eco-nomics when people are dying of hunger allaround you. Those theories appeared likecruel jokes. I became a drop-out from formalfrom the poor in the village next door to theYunus found that most villagers wereunable to obtain credit at reasonablerates, so he began by lending themmoney from his own pocket, allowingthe villagers to buy materials for proj-ects like weaving bamboo stools andmaking pots ( 1997).Ten years later, Yunus had set up thebank, drawing on lessons from informalto groups of poor households. Commonloan uses include rice processing,The groups form voluntarily, and,while loans are made to individuals, allin the group are held responsible forloan repayment. The groups consist offive borrowers each, with lending firstto two, then to the next two, and thento the fifth. These groups of five meetgroups, so that bank staff meet withrules, if one member ever defaults, allin the group are denied subsequentloans. The contracts take advantage oflocal information and the social assetsthat are at the heart of local enforce-ment mechanisms. Those mechanismsrely on informal insurance relationshipsand threats, ranging from social isola-tion to physical retribution, that facili-tate borrowing for households lackingcollateral (Besley and Coate 1995). Theprograms thus combine the scale advan-tages of a standard bank with mecha-nisms long used in traditional, group-based modes of informal finance, suchas rotating savings and credit associa-tions (Besley, Coate, and Glenn Lourymillion borrowers, 95 percent of whom$30 40 million per month. Reported re-percent, but as Section 4.2 describes,relevant rates average about 92 percentand have been substantially lower inMost loans are for one year with a(roughly a 15 16 percent real rate).Calculations described in Section 4.2suggest, however, that Grameen wouldhave had to charge a nominal rate ofaround 32 percent in order to becomecurrent cost structure constant). Themanagement argues that such an in-crease would undermine the banks so-cial mission (Shahidur Khandker 1998), In a rotating savings and credit association, agroup of participants puts contributions into a potthat is given to a single member. This is repeatedover time until each member has had a turn, withorder determined by list, lottery, or auction. Mostmicrofinance contracts build on the use of groupsbut mobilize capital from outside the area.ROSCA participants are often women, and in theU.S. involvement is active in new immigrant com-Mexicans, Salvadorans, ans, Jamaicans, Barbadans, and Ethiopians. In-volvement had been active earlier in the centuryamong Japanese and Chinese Americans, but itis not common now (Light and Pham 1998).Rutherford (1998) and Armendariz and Morduch(1998) describe links of ROSCAs and microfinance Morduch: The Microfinance Promise Grameen figures prominently as anearly innovator in microfinance and hasments of its financial performance aredescribed below in Section 4.2, of itsof its social and economic impacts in BancoSol, BoliviaBanco Solidario (BancoSol) of urbanBolivia also lends to groups but differsin many ways from Grameen. First, itsfocus is sharply on banking, not on so-cial service. Second, loans are made toall group members simultaneously, andthe solidarity groups can be formed ofthree to seven members. The bank,well. By the end of 1998, 92 percent ofthe portfolio was in loans made to soli-darity groups and 98 percent of clientswere in solidarity groups, but it is likelythat those ratios will fall over time. Bythe end of 1998, 28 percent of the port-folio had some kind of guarantee beyondThird, interest rates are relativelyhigh. While 1998 inflation was below 5percent, loans denominated in bolivi-anos were made at an annual base rateof 48 percent, plus a 2.5 percent com-mission charged up front. Clients withsolid performance records are offeredloans at 45 percent per year, but this isstill steep relative to Grameen (but notrelative to the typical moneylender,who may charge as much as 10 percentloans are denominated in dollars, how-percent per year, with a 1 percent feebank does not rely on subsidies, mak-ing a respectable return on lending.BancoSol reports returns on equity ofand returns on assets of about 4.5 per-ture. Fifth, repayment schedules areflexible, allowing some borrowers tomake weekly repayments and others todo so only monthly. Sixth, loan dura-tions are also flexible. At the end ofbetween one and four months, 24 per-seven to ten months, 19 percent haddurations of ten to thirteen months,and the balance stretched toward twothan in Bangladesh and loans are larger,with average loan balances exceeding$900, roughly nine times larger than forGrameen (although first loans may startas low as $100). Thus while BancoSolthat typical clients are among the rich-est of the poor and are clustered justabove the poverty line (where povertyis based on access to a set of basicNavajas et al. 1998). Partly this may bedue to the maturation of clients frompoor borrowers into less poor borrow-ers, but the profile of clients also looksvery different from that of the ma-ture clients of typical South AsianThe stress on the financial side hasmade BancoSol one of the key forcesin the Bolivian banking system. The The financial information is from Jean Steege,ACCION International, personal communication,January 1999. Claudio Gonzalez-Vega et al. (1997)provide more detail on BancoSol. Further infor-1576 institution started as an NGO(PRODEM) in 1987, became a bank in1992, and, by the end of 1998, served81,503 low-income clients. That scalegives it about 40 percent of borrowersPart of the success is due to impres-sive repayment performance, althoughdifficulties are beginning to emerge.Unlike most other microfinance institu-tions, BancoSol reports overdues usingconservative standards: if a loan repay-ment is overdue for one day, the entireunpaid balance is considered at risk(even when the planned payment wasonly scheduled to be a partial repay-of the portfolio was at risk at the end of1997. But by the end of 1998, the frac-that parallels a general weakeningand which may signal the negativeBancoSols successes have spawnedcompetition from NGOs, new nonbankfinancial institutions, and even formalbanks with new loan windows for low-income clients. The effect has been arapid increase in credit supply, and aweakening of repayment incentives thatmay foreshadow problems to comeStill, BancoSol stands as a financialsuccess, and the model has been repli-catedprofitablyby nine of the eigh-teen other Latin American affiliates ofACCION International, an NGO basedin Somerville, Massachusetts. ACCIONthe U.S., spread over the six programs.Average loan sizes range from $1366 inNew Mexico to $3883 in Chicago, andoverall nearly 40 percent of the clientsare female. As of December 1996, pay-ments past due by at least thirty daysaveraged 15.5 percent but ranged ashigh as 21.2 percent in New York and32.3 percent in New Mexico. ACCIONsother affiliates, including six in the UnitedStates, have not, however, achieved fi-nancial sustainability. The largest im-pediments for U.S. programs appear tobe a mixed record of repayment, andstitutions from charging interest rates Rakyat IndonesiaLike BancoSol, the Bank Rakyat In- system is financiallyself-sufficient and also lends to betteroff poor and nonpoor households, withaverage loan sizes of $1007 during1996. Unlike BancoSol and Grameen,lending mechanism. And, unlike nearlyall other programs, the bank requiresindividual borrowers to put up collat-eral, so the very poorest borrowers areexcluded, but operations remain small-scale and collateral is often definedloosely, allowing staff some discretion toincrease loan size for reliable borrowerswho may not be able to fully back loanswith assets. Even in the wake of the re-cent financial crisis in Indonesia, repay-ment rates for BRI were 97.8 percent incost reductions by setting up a network Data are from ACCION (1997) and hold as ofDecember 1996. Five of the six U.S. affiliates haveonly been operating since 1994, and the group as awhole serves only 1,695 clients (but with capitalsecured for expansion). A range of microfinanceinstitutions operate in the U.S. Among the oldestand best-established are Chicagos South ShoreBank and Bostons Working Capital. The Cal-Meadow Foundation has recently provided fund-ing for several microfinance programs in Canada.Microfinance participation in the U.S. is heavilyminority-based, with a high ethnic concentration.For example, 90 percent of the urban clients ofBostons Working Capital are minorities (and 66percent are female). Loans start at $500. Clientstend to be better educated and have more job ex-29 percent of Working Capitals borrowers were Morduch: The Microfinance Promise of branches and posts (with an averageof five staff members each) and nowserves about 2 million borrowers and 16million depositors. (The importance ofSection 7.) Loan officers get to knowwith small loans and increasing loansize conditional on repayment perfor-mance. Annualized interest rates are 34percent in general and 24 percent ifloans are paid with no delay (roughly 25percent and 15 percent in real termsitself as a social service organization,training or guidanceit aims to earn abusiness (Marguerite Robinson 1992). program$175 million in profits on their loans tolow-income households. More striking,have exceeded the performance of loansmade to corporate clients by other parts programdid not have to cross-subsidize the restof the bank, they could have brokeneven in 1995 while charging a nominalyear on loans (around a 7 percent realrate; Jacob Yaron, McDonald Benjamin, Kredit Desa, IndonesiaThe Bank Kredit Desa systemtution to BRI, is much less well-known.The program dates back to 1929, al-though much of the capital was wipedout by the hyper-inflation of the middleloans are made to individuals and theoperation is financially viable. At the endof 1994, the BKDs generated profits of$4.73 million on $30 million of net loansLike Grameen-style programs, theand scale is small, with an emphasis on$71 in 1994. The term of loans is gener-ally 10 12 weeks with weekly repay-ment and interest of 10 percent on theprincipal. Christen et al. (1995) calcu-late that this translates to a 55 percentnominal annual rate and a 46 percentreal rate in 1993. Loan losses in 1994were just under 4 percent of loansAlso as in most microfinance programs,novation of the BKDs is to allocatefunds through village-level managementcommissions led by village heads. Thisworks in Indonesia since there is a clearsystem of authority that stretches fromJakarta down to the villages. The BKDsmanagement commissions thus build inmany of the advantages of group lend-ing (most importantly, exploiting localinformation and enforcement mecha-nisms) while retaining an individual-lending approach. The commissions arebut appear to be relatively democraticin their allocations. Through the latecapital for lending and hold balances inBRI accounts. The BKDs are now su-pervised by BRI, and successful BKD Village BanksProspects for replicating the BKDsever. A more promising, exportable Figures are calculated from Johnston (1996)1578 village-based structure is provided bythe network of village banks started inthe mid-1980s in Latin America bynity Assistance (FINCA). The villagebanking model has now been replicatedin over 3000 sites in 25 countries byNGOs like CARE, Catholic Relief Ser-vices, Freedom from Hunger, and Savethe Children. FINCA programs aloneserve nearly 90,000 clients in countriesUganda, and Kyrgyzstan, as well as inMaryland, Virginia, and Washington,The NGOs help set up village finan-cial institutions in partnership with lo-cal groups, allowing substantial localautonomy over loan decisions and man-agement. Freedom from Hunger, forexample, then facilitates a relationshipbetween the village banks and local com-mercial banks with the aim to createThe village banks tend to serve apoor, predominantly female clientelesimilar to that served by the GrameenBank. In the standard model, the spon-soring agency makes an initial loan tothe village bank and its 30 50 members.Loans are then made to members, start-ing at around $50 with a four monthterm, with subsequent loan sizes tied tothe amount that members have on de-posit with the bank (they must typicallyhave saved at least 20 percent of theloan value). The initial loan from thesponsoring agency is kept in an exter-nal account, and interest income isused to cover costs. The deposits ofmembers are held in an internal ac-count that can be drawn down as de-positors need. The original aim was tobuild up internal accounts so that exter-three years, but in practice growingcredit demands and slow savings accu-lage banks successfully harness local in-formation and peer pressure without us-ing small groups along BancoSol orGrameen lines. And, as with the BKDs,terest rates as high as 4 percent permonth. Most village banks, however,still require substantial subsidies tocover capital costs. Section 4.1 showsevidence that village banks as a groupcover just 70 percent of total costs onlage banks have been set up in areasthat are particularly difficult to serve(e.g., rural Mali and Burkina Faso), andthe focus has been on outreach rather Microfinance MechanismsThe five programs above highlightthe diversity of approaches spawned bythe common idea of lending to low-income households. Group lending hasidea has had immediate appeal for eco-nomic theorists and for policymakerswith a vision of building programswhen physical assets are few. But itsrole has been exaggerated: group lend-ing is not the only mechanism that dif-ferentiates microfinance contracts from The programsdescribed above also use dynamic in-and collateral substitutes to help main-tain high repayment rates. Lending to Ghatak and Guinnane (1999) provide an excel-Huppi and Gershon Feder (1990) provide an earlyperspective. Armendariz and Morduch (1998) de- Morduch: The Microfinance Promise women can also be a benefit from aAs shown in Table 1, just two of thetracts, but all lend in increasingamounts over time (progressive lend-ing), offer terms that are substantiallybetter than alternative credit sources,payments, beginning soon after loan re-ceipt. While we lack good evidence onnisms, there is increasing anecdotal evi-(e.g., the village studies from Bangla-Matin 1997; Woolcock 1999; Sanae Ito1998; and Pankaj Jain 1996). This sec-tion highlights what is known (or oughtto be known) about the diversity oftechnologies that underlie repayment Peer Selectioncreated by adverse selection. The key isthat group-lending schemes provide in-centives for similar types to group to-gether. Ghatak (1999) shows how thissorting process can be instrumental inimproving repayment rates, allowing forlower interest rates, and raising socialwelfare. His insight is that a group-discriminate that is impossible with anTo see this, imagine two types of po-tential investors. Both types are riskneutral, but one type is risky and theother is safe; the risky type fails moreoften than the safe type, but the riskytypes have higher returns when success-ful. The bank knows the fraction ofeach type in the population, but it isunable to determine which specific in-vestors are of which type. Investors,though, have perfect information aboutwith an uncertain outcome that requiresundertake the project, they can earn. The risky investors have and net re-. The safe investors have a prob- and net return When either type fails, the return is zero.Risky types are less likely to be suc- but they have higher re-turns when they succeed. For simplic-ity, assume that the expected netreturns are equal for both safe and risky The projects ofboth types are socially profitable in thatexpected returns net of the cost of capi-Neither type has assets to put up ascollateral, so the investors pay the banknothing if the projects fail. To breakeven, the bank must set the interestrate high enough to cover its per-loanequilibrium interest rate under compe-tition will then be set so that is the average probability ofsuccess in the population. Since thebank cant distinguish between borrow-ers, all investors will face interest rate,. As a result, safe types have lower ex-pected returns than risky typessinceand the safe types will If the safe types enter,But the safe types will stay out of the and only riskytypes might be left in the market. Inthat case, the equilibrium interest rate Armendariz and Gollier (1997) also describe1580 out the safe. The risky types lose theimplicit cross-subsidization by the safeefficient since only the risky types bor-row, even though the safe types alsoCan a group-lending scheme improveon this outcome? If it does, it mustbring the safe types back into the mar-two people, with each group formeddently, but the contract is written tosuch that each borrower pays nothing ifher project fails, and an amount ifher project is successful. In addition, if the other mem- The expectednet return of a safe type teamed with arisky type is then with similar calculations for exclusivelyWill the groups be homogeneous orferred as partners (since their prob-ability of failure is lower), the questionbecomes: will the risky types be willingto make a large enough transfer to thesafe types such that both risky and safetypes do better together? By comparingnarios, we can calculate that a safe typewill require a transfer of at least to agree to form a partner-ship with a risky type. Will risky typespected net gain from joining with a safe, the expected gains to risky typesare always smaller than the expectedlosses to safe types. Thus, there is nosafe types to group together. Grouplending thus leads to assortative match-ing: all types group with like typesHow does this affect the functioningdemonstrates that the group-lendingcontract provides a way to charge dif-ferent effective fees to risky and safeactly the same contract with exactly thesame nominal charges, and result arises because risky types will beteamed with other risky types, whiletypes then receive expected net returns while safe typesreceive expected net returns of. Thus, a successfulrisky type is more likely to have to pay than a and are setappropriately, the group-lending con-tract can provide an effective way toprice discriminate that is impossibleunder the standard second-best indi- 0.9 and 0.8, for example, the safer typescan expect to pay less than the riskiertypes as long as the joint liability 1.4Efficiency gains result if the differenceis large enough to induce the safe typesback into the market. When this hap- In typical contracts, group members are re-sponsible for helping to pay off the loan in diffi-culty, rather than having to pay a fixed penalty forlateral, they are assumed to have a large enoughincome flow to cover these costs if needed. Inpractice this may impose a constraint on loan sizesince individuals may have increasing difficultypaying Ghatak (1998) extends the results to groupslarger than 2, a continuum of types, and prefer-ences against risk. See also Varian (1990) and Ar-mendariz and Gollier (1997) on related issues of Morduch: The Microfinance Promise Peer MonitoringGroup lending may also providebenefits by inducing borrowers not totake risks that undermine the banksprofitability (Stiglitz 1990; Besley andCoate 1995). This can be seen byslightly modifying the framework inInstead, consider identical risk averseEach borrower may do either risky orsafe activities, and each activity againtion about borrowersin particular,here it cannot tell whether the borrow-ers have done the safe or risky activity.Moral hazard is thus a prime concern.When projects fail, borrowers have a re-lending contract. Borrowers either have or depending on whether they do the safeor risky activity. If everyone did the = and break even.But, since the bank cannot see whichtract on it), borrowers may fare betterdoing the risky activity and getting ex-risky activity and getting ex-Usr] = pru(Rr /ps). Thebank then loses money. Thus, the bankraises interest rates to = borrower gets expected utility ofgets expected utility ofUrr] = pru(Rr /pr), and she is clearlyworse off than with a lower interestrate. In fact, if the borrower couldtivity, she could be better offwith ex-with ex-Uss] = psu(Rs /ps). Thusthe borrower prefers E[Usr] to E[Uss] toE[Urr], but the information problemand inability to commit means that sheinability to commit means that sheUrr].How can a group-lending contractimprove matters? The key is that it cancreate a mechanism that gives borrow-ers an incentive to choose the safe ac-tivity. Again consider groups of two bor-those in Section 3.1 above. The borrow-ers in each group have the ability toenforce contracts between each other,and they jointly decide which typesproblem is to choose between both do-ing the safe activity, yielding each bor-rower expected utility of , or doing therisky activity with expected utility is set highbank, though, knows borrowers will nowdo the safe activity, and it earns extraincome from the joint-liability pay-ments. The bank can thus afford tolower the interest rate to offset theThus, through exploiting the abilityof neighbors to enforce contracts andmonitor each othereven when thebank can do neitherthe group-lendingcontract again offers a way to lowerequilibrium interest rates, raise expectedutility, and raise expected repayment Dynamic IncentivesA third mechanism for securing highrepayment rates with high monitoringcosts involves exploiting dynamic incen-tives (Besley 1995, p. 2187). Programstypically begin by lending just smallamounts and then increasing loan sizepeated nature of the interactionsandlending when loans are not repaidcan1582 be exploited to overcome informationproblems and improve efficiency,whether lending is group-based orIncentives are enhanced further ifborrowers can anticipate a stream of in-Mosley 1996 term this progressivecalls it step lending.) As above, keep-ing interest rates relatively low is criti-cal, since the advantage of microfinanceprograms lies in their offering servicescompetitors rates. Thus, the Bank Rak-yat Indonesia (BRI) and BancoSolcharge high rates, but they keep levelswell below rates that moneylendersthe power of the dynamic incentivesboth the Bank Rakyat Indonesia andBancoSol are starting to feel as othercommercial banks see the potentialprofitability of their model. In practice,felt by most microfinance institutions(perhaps because so few are actuallyturning a profit). As competition grows,the need for a centralized credit ratingDynamic incentives will also workbetter in areas with relatively low mo-bility. In urban areas, for example,where households come and go, it maynot be easy to catch defaulters whomove across town and start borrowingagain with a clean slate at a differentbranch or program. BRI has facedtheir urban programs than in their ruralones, which may be due to greaterrepeated games. If the lending relation-ship has a clear end, borrowers have in-centives to default in the final period.Anticipating that, the lender will notlend in the final period, giving borrow-ers incentives to default in the penulti-mate periodand so forth until the en-tire mechanism unravels. Thus, unlessthe end dateor if graduation from one), dynamic incentives haveOne quite different advantage of pro-gressive lending is the ability to testborrowers with small loans at the start.relationships with clients over time andexpanding loan scale (Parikshit GhoshDynamic incentives can also help toexplain advantages found in lending towomen. Credit programs like those ofthe Grameen Bank and the Bangladeshdid not begin with a focus on women.and 34 percent of their respective mem-berships, but by 1991 92, BRACsmembership was 74 percent female andGrameens was 94 percent female (AnneMarie Goetz and Rina Sen Gupta 1995).also focus on lending to women, and itappears to confer financial advantageson the programs. At Grameen, for ex-ample, 15.3 percent of male borrowerssome payments before the final duedate) while this was true for just 1.3percent of women (Khandker, BaquiThe decision to focus on women hassome obvious advantages. The lowermobility of women may be a plus where See the general theoretical treatment in Bol-ton and Scharfstein (1990) and the application to Morduch: The Microfinance Promise moral hazard is a problem (i.e.,where there is a fear that clients willtake the money and run). Also, wherewomen have fewer alternative borrow-ing possibilities than men, dynamicThus, ironically, the financial successof many programs with a focus on lackof economic access of women, while, atthe same time, promotion of economic Regular Repayment SchedulesOne of the least remarked uponbutnance credit contracts is that repay-ments must start nearly immediately af-ter disbursement. In a traditional loancontract, the borrower gets the money,invests it, and then repays in full withGrameen-style banks, terms for a year-long loan are likely to be determined byadding up the principal and interest duein total, dividing by 50, and startingweekly collections a couple of weeks af-ter the disbursement. Programs likeBancoSol and BRI tend to be more flex-ible in the formula, but even they donot stray far from the idea of collectingrepayment schedules screen out undis-ciplined borrowers. They give earlywarning to loan officers and peer groupmembers about emerging problems. TABLE 2NDICATORS Average loanbalance ($) Avg. loan as Average Average All microfinance institutions724153410583 Fully sustainable3442839139113 Individual lending308427612092 Solidarity groups204513510389 Village bank2294119169 Low end37133138872 Broad2856448122100 High end7297135912176 3 to 6 years15301449884 7 or more years403742712398 Statistical appendix to (1998). Village banks have a B data quality; all others areAverages exclude data for the top and bottom deciles. Rahman (1998) describes complementary cul-tural forces based on womens culturally pat-terned behavior. Female Grameen Bank borrow-ers in Rahmans study area, for example, are foundto be much more sensitive to verbal hostilityheaped on by fellow members and bank workerswhen repayment difficulties arise. The stigma isexacerbated by the public collection of paymentsat weekly group meetings. According to Rahman(1998), women are especially sensitive since theirmisfortune reflects poorly on the entire household(and lineage), while men have an easier time shak-1584 And they allow the bank to get hold ofcash flows before they are consumed orotherwise diverted, a point developedprocess begins before investments bearthat the household has an additional in-come source on which to rely. Thus, in-that the bank is effectively lendingpartly against the households steady,risky project. This confers advantagesfor the bank and for diversified house-holds. But it means that microfinancehas yet to make real inroads in areas fo-cused sharply on highly seasonal occu-pations like agricultural cultivation.challenges to the spread of microfi-nance in areas centered on rainfedagriculture, areas that include some ofthe poorest regions of South Asia and Collateral SubstitutesWhile few programs require collat-eral, many have substitutes. For exam-ute to an emergency fund in therowed (beyond a given scale). Theemergency fund provides insurance incases of default, death, disability, etc.,membership. An additional 5 percent ofthe loan is taken out as a group taxthat goes into a group fund account. Upto half of the fund can be used by groupTypically, it is disbursed among thegroup as zero-interest loans with fixedterms. Until October 1995, GrameenBank members could not withdrawthese funds from the bank, even uponleaving. These forced savings can nowter the banks have taken out what they TABLE 2 ( Avg. return Avg. percent of Avg. percent Avg. number of All microfinance institutions 8.53.3659,035 Fully sustainable9.32.66112,926 Individual lending 5.03.15315,226 Solidarity groups 3.04.1497,252 Village bank 17.42.8927,833 Low end 16.23.8747,953 Broad1.23.06012,282 High end 6.21.9341,891 3 to 6 years 6.82.2719,921 7 or more years 2.44.16316,557 Morduch: The Microfinance Promise are owed. Thus, in effect, the fundsadvocates, however, emphasize insteadating repayments (Richard Patten andJay Rosengard 1991; Robinson 1992). Itis impossible, though, to determine eas-important in driving repayment rates.does not signal its lack of importance asan incentive device. If the threat of col-lection is believable, there should befew instances when collateral is actuallyBancoSol also stresses the role ofments, but as its clients have prosperedat varying rates, lending approachescent of its portfolio had some kind of Empirical Research Agendaadvertised? Is there evidence of assorta-tive matching through group lending asperformance, as suggested by the the-ory of dynamic incentives? Extendinging contract heighten default prob-abilities for the entire group when somemembers run into difficulties, as pre-dicted by Besley and Coate (1995)?Does group lending lead to excessive(Banerjee, Besley, and Guinnaneless flexible than individual lending forborrowers in growing businesses andthose that outstrip the pace of theirpeers (Madajewicz 1997; Woolcockcostly (for both borrowers and bankand at busy agricultural seasons? Do so-cial programs enhance economic perfor-mance? When default occurs, do bankstaff follow the letter of the law and cutoff good clients with the misfortune tobe in groups with unlucky neighbors?Or is renegotiation common (Hashemiand Sidney Schuler 1997; Matin 1997;are supported with anecdotes from par-ticular programs, but they have notbeen established as empirical regulari-both the growing body of microfinanceEmpirical understandings of microfi-nance will also be aided by studies thatquantify the roles of the various mecha-nisms in driving microfinance perfor-mance. The difficulty in these inquiries isthat most programs use the same lend-no variation off of which to estimate theefficacy of particular mechanisms. Well-designed experiments would help (e.g.,individual-lending contracts to some ofthe sample, group-lending contracts toothers; weekly repayments for some,monthly or quarterly schedules for others).Lacking well-designed experiments, acollection of studies instead presentsregressions in which repayment ratesare explained by proxies for forces be-hind particular mechanisms. The vari-ation thus arises from features of theefficacy of particular program features:competitive are credit markets? Howanisms? The variation in answers to1586 mation, but the evidence is indirect andkets, and enforcement mechanisms isunlikely to matter only through theform of credit contract. In addition, se-lection biases of the sort raised in Sec-tion 6.1 are likely to apply. Still, someFor example, Wydick (1999) reportson a survey of an ACCION Interna-tional affiliate in western Guatemalatailored to elicit information aboutgroups. He finds that improvements inrepayment rates are associated withvariables that proxy for the ability tomonitor and enforce group relation-sales of fellow group members. Hefinds little impact, though, of social tiesable group members than others. In fact,members are sometimes softer on theirfriends, worsening average repaymentMark Wenner (1995) investigates re-payment rates in 25 village banks infinds active screening that successfullyexcludes the worst credit risks, workingin a more straightforward way than inthe simple model of peer selection inSection 3.1 above. He also finds thatdelinquency rates are higher in betteroff towns. This lends support to the the-ory of dynamic incentives: where bor-likely to value the programs less, andThe result is echoed by Manoharstudy of three programs in Bangladesh(but not Grameen). They find that re-payment rates are higher in remotecommunitiesi.e., those with fewer al-al. (1995, Table 7.2), however, find thedesh banks (including Grameen). BothThis may be a product of improved li-quidity and better business opportuni-ties in better-off villages, but it mightThese bits of evidence show thatgroup lending is a varied enterprise andthat there is much to microfinance be-between theory and evidence will be animportant step toward improving and Profitability and FinancialMicrofinance discussions pay surpris-ingly little attention to particular mech-cordingly, this section considers fi-nances, and social issues are taken upHow well in the end have microfi-promise? A recent survey finds 34 prof-itable programs among a group of 72with a commitment to financial sus-1998). This does not imply, however,self-sufficient. The hundreds of pro-grams outside the base 72 continue todepend on the generosity of donorsreplicators do not make the list of 72,although BancoSol and BRI do). Someexperts estimate that no more than 1are currently financially sustainableand perhaps another 5 percent of NGO The figures are based on an informal polltaken by Richard R Morduch: The Microfinance Promise The other 95 percent of programs inoperation will either fold or continuecosts are high or because they choose tocosts on to their clients. Although subsi-dies remain integral, donors and practi-tioners have been reluctant to discussperhaps for fear of appearing retro-grade in light of the disastrous experi-ences with subsidized government-run International EvidenceTable 2 gives financial indicators forthe 72 programs in the survey. The 72 programs havebeen divided into non-exclusive catego-ries by age, lending method, targetgroup, and level of sustainability.(There is considerable overlap, for ex-ample, between the village bank cate-gory and the group targeting low endThe groups, divided by lendingmethod and target group, demonstratethe diversity of programs marching be-hind the microfinance banner. Averageloan balances range from $94 to $842when comparing village banks to thosefocus on women varies from 92 percentto 53 percent. The target group cate-gory makes the comparison starker,with average loan balances varying from$133 to $2971. Averages for the 34 fullyever, substantially different from theoverall sample in terms of average loanbalance or the percentage of femaleSustainability is generally considered This refers to the abilityof institutions to generate enough reve-nue to cover operating costsbut notnecessarily the full cost of capital. Ifunable to do this, capital holdings are Thisorder to operate. If the institution isnot financially sustainable, it cannotsurvive if it has to obtain all inputs (es-pecially capital) at market, rather thanMost of the programs in the surveyability hurdle. The only exceptions arethe village banks and those with lowend targets, both of which generateabout 90 percent of the requiredMany fewer, however, can cover fullcapital costs as well. Overall, programsgenerate 83 percent of the required in-get groups generate about 70 percent.Strikingly, the handful of programs thatfocus on high end clients are just asheavily subsidized as those on the lowend. Similarly, the financial perfor- The project started as a collaboration with theAmerican Economic Associations Economics In- Those with low end target groups have aver-age loan balances under $150 or loans as a per-include, for example, FINCA programs). Thosewith broad targets have average balances that areBancoSol and the BRI unit desa system). The highpercent of GNP per capita. The solidarity groupmethodology is based on groups with 3 5 borrow-ers (like BancoSol). The village banks have groups See Mark Schreiner (1997) and Khandker(1998) for discussions of alternative views of sus-tainability. Unlike other reported figures, thosehere make adjustments to account for subsidies oncapital costs, the erosion of the value of equity dueto inflation, and adequate provisioning for non-re-coverable loans. To the extent possible, the figuresare comparable to data for standard commercial1588 loans is roughly equivalent to that ofthough the former serve a clientele thatThe greatest financial progress hasbeen made by broad-based programslike BancoSol and BRI that serve cli-ents across the range. Financial pro-gress also improves with age (althoughcan only be suggestive as their orienta-The returns to equity echo the dataon financial sustainability. The numbersinto the programs. The table shows thatthis is not a place to make big bucks.programs are respectable, they do notcompete well with alternative invest-ments and often carry considerable risk.At the same time, social returns maywell be high even if financial returnsare modest (or negative). On average,the broad-based programs, for example,clients with modest incomes, most ofsurely pass by the investment opportu-creased through more effective leverag-ing of equity, however, Wall Street mighteventually be willing to take a look. In-creasing leverage is thus the cuttingtheir original focus on how to makemicrofinance, achieving financial sus-tainability and increasing returns to eq-uity is the only game to play. The issue is:will donors tire if social returns can beproven to justify the costs? Answeringthe question puts impact studies and cost benefit analyses high on the research The Grameen Bank ExampleThe data above have been adjusted tobring them into rough conformity withstandard accounting practices. This isoften calculated in idiosyncratic waysand are vulnerable to misinterpretation.open with its data, and it provides a fullTable 3 provides evidence on the The table shows Gra-meens rapid increase in scale, with thesize of the average annual loan portfolioincreasing from $10 million in 1985 toexpanded 12 times over the sameThe bank reports repayment ratesabove 98 percent and steady profits 1997). All accounting defi-nitions are not standard, however. Thereported overdue rates are calculatedby Grameen as the value of loans over-due greater than one year, divided by None of the U.S. programs that I know of areprofitable, and some are very far from financialsustainability, held back by legal caps on interestrates (Michael Chu 1996). None of the U.S. pro- The base data are drawn from Grameen Bankannual reports. This section draws on Morduch(1999). Summaries of Grameens financial perfor-mance through 1994 can be found in Hashemi andSchuler (1997) and Khandker, Khalily, and Kahn(1995). Schreiner (1997) provides alternative cal-culations of subsidy dependence with illustrationsfrom Grameen. The adjustments here capture themost critical issues, but they are not comprehen- Morduch: The Microfinance Promise the current portfolio. A problem is thatthe current portfolio tends to be muchlarger than the portfolio that existedmade. With the portfolio expanding 27times between 1985 and 1996, reporteddefault rates are considerably lowerthan standard calculation of arrearsthe share of the portfolio at risk). Thewith the size of the portfolio at the timeDoing so can make a big difference:overall, overdues averaged 7.8 percentbetween 1985 and 1996, rather than theimpressive relative to the performanceof government development banks, butit is high enough to start creating finan-cial difficulties. More dramatically, thebank reported an overdue rate of 0.8percent in 1994, while at the same timeSimilarly, reported profits differ con-siderably from adjusted profits in Tablequate provision for loan losses. Until re-cently, the bank had been slow to writeoff losses, and the adjusted rates ensurethat in each year the bank writes off amodest 3.5 percent of its portfolio (still,average overdue rate). The result islosses of nearly $18 million between1985 and 1996, rather than the banks TABLE 3RAMEENNDICATORS(Millions of 1996 U.S. dollars) 1985 1990 1992 1994 1996 Average annual loans outstanding10.058.383.8211.5271.3108 Members (thousands)1728701,4242,0132,0601,101 Reported overdues rate2.83.32.50.813.91.6 Adjusted overdues rate3.86.21.915.07.8 Reported profits0.020.09 0.150.560.461.5 Adjusted profits 0.33 1.51 3.06 0.93 2.28 17.8 Direct grants0.02.31.72.02.116.4 Value of access to soft loans1.17.05.89.012.780.5 Value of access to equity0.00.42.78.08.847.3 Subsidy per 100 units outstanding1121167911 Average nominal on-lending rate16.811.115.816.715.915.9 Average real on-lending rate5.93.011.613.110.110.1 Benchmark cost of capital Average nominal cost of capital Subsidy dependence index80263106456574 Avg. nominal break-even rate30.240.232.624.226.225.7 A: average for 1985 94, weighted by portfolio size. B: Sum for 1985 96.1590 Grants from donors are consideredpart of income in the profit calcula-tions. If the bank had to rely only onincome from lending and investments,it would have instead suffered losses ofThe bulk of the banks subsidies en-borrowed capital (a 1.7 percent realrate). Had it not had access to conces-sional rates, it would have had to payconsiderably more. Here, an alternativeproximated as the Bangladesh deposit (1996) plus a 3 percent adjust-ment for transactions costs. The differ-ence in rates yields a total value of ac-cess to soft loans of $80.5 millionbetween 1985 and 1996. An additionalimplicit subsidy of $47.3 million was re-ceived by Grameen through access toequity which was used to generateAlthough subsidies have increasedover time in absolute quantities, thebanks scale has grown even morequickly. As a result, the annual subsidyper dollar outstanding has fallen sub-marizes the subsidy data by yielding anestimate of the percentage increase inthe bank to operate without subsidies ofany kind (Yaron 1992). The result for1985 96 indicates that in the early1990s Grameen would have had to in-crease nominal interest rates on its gen-eral loan product from 20 percent toabove 50 percent. Overall, the averagebreak-even rate is 32 percent (the aver-age on-lending rate is lower than 20portfolio is comprised of housing loansoffered at 8 percent interest per year).While borrowers would not be happy, itis not obvious that they would defect.Clients of the Bangladesh Rural Ad-vancement Committee, a Grameencompetitor with a similar client base,are already paying 30 percent nominaldown administrative costs would pro-salary and personnel costs accountedfor half of Grameens total costs, whilesible since they are linked to govern-ment wage scales, so the emphasis hashad to be on increasing efficiency. By1996, salary and personnel costs wereroughly equal to interest costs (Mor-duch 1999). Training costs have also1991, 54 percent of female trainees andbefore taking up first positions withGrameenand much of Grameens di-rect grants are funneled to supportingtraining efforts (Khandker, Khalily, andThe Association for Social Advance-ment (ASA), another large microfinancepresence in Bangladesh, demonstrates amore radical approach to cost control.They have streamlined record keepingloans, housing loans, collective loans,seasonal loans, and, more recently,comfortable hiring staff with fewer for-mal qualifications than Grameen, andeliminated mid-level branch offices andhas centered nearly exclusively on thelarger groups of forty village members,The Grameen Banks current path, pur-suing cross-subsidization and alternative Morduch: The Microfinance Promise income generation projects (includingan internet provision service and otherfor-profit spin-offs) is appealing in themedium term, but it has its own perils:the banks mission risks getting diluted,and profitable sectors are vulnerable tohave been exaggerated, but even if thebank is not the economic miracle thatthat its failure to reach financial self-sufficiency is in itself a problem. Ascosts and donors remain committed tothe cause, Grameen could hold up as asustainability as a key principle. At thesubsidies of one sort or another. Theseporary aids that help programs over-come start-up costs, not as ongoing pro-a series of worries. First, donors can befickle, and programs that aim to existinto the future feel the need for inde-pendence. Second, donor budgets arelimited, restricting the scale of opera-cient programs, on the other hand, canexpand to meet demand. Third, subsi-dized programs run the risk of becom-ing inefficient without hard bottomlines. Fourth, in the past subsidies haveended up in the wrong hands, ratherThe view that subsidies should just benot been an important part of microfi-nance practice, and there have been nocareful cost-benefit studies to date. Butthe fact is that subsidies are an ongoingreality: some infants are getting old.problems associated with subsidies canbut governments will remain committedto poverty alleviation well after interna-tional agencies have moved on to thenext Big Idea. If subsidized microfi-nance proves to deliver more bang forthe buck than other social investments,Scale certainly matters, but often asmall well-targeted program may do(2000). Assume that the typical client ina subsidized program has an income of,say, 50 percent of the poverty line,while the typical client of a sustainable(high interest rate) program has an in-come of 90 percent of the poverty line.To clarify the comparison, assume thatare identical for the programs (afterthe commonly-used squared povertygap of James Foster, Joel Greer, andsuggests that raising the poorer bor-rowers income by one dollar has fivetimes greater impact than doing the(roughly the size of Bolivias BancoSolin the early 1990s), the subsidized pro-clients to have an equivalent impact. The This section draws heavily on Morduch(2000). Adams, Graham, and von Pischke (1982)present a well-argued alternative perspective.Schreiner (1997) presents a framework for consid-ering cost-effectiveness applied to BancoSol and1592 comparison is too simple, but it amplyillustrates how social weights and depthof outreach can outweigh concerns withThe third issue, the danger of slip-strated many times over by large publicbanks in low-income countries. But thekey to efficiency is the maintenance ofhard budget constraints, not necessarilyand explicit performance targets whenconditioning future tranches on perfor-mances to date. The lessons can be ap-plied more widely and used to promoteefficiency and improve targeting in a Simple Cost Benefit Ratioscompared? A simple gauge can beformed by dividing the value of subsi-dies by a measure of benefits accruingto borrowers. For example, Khandker(1998) reports a cost benefit ratio of0.91 with respect to improvements infor every dollar of benefit to clients. Asimilar calculation leads to a cost bene-fit ratio of 1.48 for borrowing by men.men appears to have a smaller impacton household consumption (Mark Pittand Khandker 1998), but Khandkerstresses that even the ratio for maleborrowers compares favorably to alter-native poverty alleviation programs inBangladesh, like the World Food Pro- 1.71) and CAREs simi- 2.62).The microfinance programs of theBangladesh Rural Advancement Com-tios of 3.53 and 2.59 for borrowing fromThese calculations provide an impor-tant first-cut at taking costs and bene-fits seriously. They suggest that invest-ing in microfinance is not a universalwinner, but some programs beat alter-natives. Like all quick calculations,though, they rest on a series of simplifi-surable benefits can be considered, thusexcluding much-discussed social im-pacts like gender empowerment.Other limits hinge on how the measur-able impacts are quantified. For exam-ple, the 0.91 ratio for lending to womencent increase in household consumptionfor every additional dollar borrowed byker 1998). The estimate is a marginalimpact of an additional dollar lent, butthe average impact is more appropriatehere since the entire program is beingevaluated, not just the expansion of If average benefits were usedinstead and if marginal returns diminishwith amounts borrowed, the cost bene-greater impact than $1 benefit for each$0.91 spent. On the other hand, if therenologies, marginal returns may well behigher than average returns, weakeningsupport for Grameen. There is evidenceto suggest that this may be the case: as impacts estimated with the samePutting aside the average-marginal The econometric structure required for iden-tification in fact rests on the assumption that mar-tion 6.3 below), although Pitt and Khandkerinterpret the impacts as marginal. Average impactsestimated with more limited econometric struc-ture turn out to look very different (Morduch Morduch: The Microfinance Promise distinction, simple cost benefit ratiosfail to capture dynamics. Imagine thatborrowing allows a client to purchase a(and being able to set up a small-scaletailoring business) creates benefits intothe future, and using impacts on cur-rent household consumption does notsince cost is a stock variable, whileshould be compared to the presentthe current impact, and doing so willPerhaps the most difficult problemand the one most relevant from the van-tage of the current debate into microfi-the relevant counterfactual scenario.Cost benefit ratios might be improved(or worsened) by reducing subsidiestios provide no sense of the optimalitythat a dollar used to subsidize an exist-ing microfinance program helps poorhouseholds more than other uses, it be that the microfinanceprogram would ultimately help more subsidized (orif it was subsidized at a much lowerlevel). This kind of argument has beenput forward often by observers skepticalAgain consider the hypothetical com-parison above of a sustainable programwith 63,000 clients versus a subsidizedprogram with just 12,600. If societypoor borrower is worth just ten timesthe value of a dollar to a less poor bor-rower (rather than 5 times the value asassumed above), the larger (nonsubsi-dized) program will now do more to im-prove social welfare than the one withsubsidies. Resolving the issues requiresmaking explicit social valuations andThe assertion that borrowers desire to credit, not subsidized credit,misses another aspect that militatesagainst subsidies. The example assumesthat the subsidized program reachesmuch poorer clients, but if it is truenot very sensitive to the interest rate,the profile of borrowers should be simi-programs. Pushing for financial sustain-ability should not limit the depth ofoutreach by much, and the case forhowever, tends to rely on either partialcapital when all else is not held con-stant) or incomplete views of demandconditions (e.g., seeing demand at highof potential borrowers that are discour-aged by high costs). Some argue that in-absorb high interest rates. But, all the profits could remain sensitivePractitioners in Bangladesh tend tobelieve that the elasticity of credit de-mand with respect to the interest rate ishigh, and accordingly they keep interestreal). Practitioners in Latin Americatend to believe that the elasticity is low,and they set interest rates as high asneeded (approaching 60 percent real).Both could be correct in their contexts,The issue relates to a broader con-cern with impacts on non-borrowers.Specifically, why is it assumed that a must be made between program1594 types? Why cant different types of pro-grams coexist? More generally, how willthe existence of a subsidized programaffect the profitability of both formaland informal institutions operating(1998) and Pinaki Bose (1998), for ex-ample, illustrate cases in which the en-try of a subsidized program worsens theby moneylenders in the informal sector.The negative impacts occur because thesubsidized programs reduce optimalscale and siphon off the best borrowers,leaving the non-subsidized lenders witha riskier pool of clients and higher en-(1999) similarly considers the case inwhich clients might borrow simultane-ously from both formal and informalof the formal sector outweigh the infor-of cases in which the clients of subsi-expense of borrowers (and lenders)describe the possibility of favorablecounter-examples in which everyonebenefits. In this line, Maria Floro, andRay (1997) and Gabriel Fuentes (1996)provide cases in which increasing for-mal sector credit may eventually per-colate down to the informal sector,increasing credit availability there asmakers have little to go on beyond Empirical Research AgendaThe issues above can be put togetherformally to show the kinds of informa-tion that are needed to put numberson the ideas under debate. The start-ing point is a social welfare function, which is assumedto be additively separable and indexedover the entire population isa measure of the lifetime welfare ofThe total amount borrowed from all, and the borrowers aver-age return per unit is Borrowers pay an average interest rate, depending on the sources of loans.), and I assume that house-hold welfare is derived from base in- plus income from borrowing:). The change in socialwelfare for a small decrease in subsidi- = ii = dyi dLi ri) +i di To see the key issues most easily, I ignore theheterogeneity in capital and non-credit serviceslike savings. In thinking about the place of subsi-dized microfinance institutions more generally, wewould also want to consider impacts on the funda-lages: the role microfinance can play in empower-that many microfinance programs may make thegreatest impacts, but it is also the set of impactsthat are hardest to measure. The focus is on im-pacts on poverty rather than purely efficiencyal- Morduch: The Microfinance Promise tention and priorities for empirical re-search. The first issue, moving from theleft, is the need to make explicit socialjudgements about the distribution of so-knowledge of the baseline welfare levelsof all householdsa critical determinantof how income affects welfare, baseline income levels and demographiccharacteristics of both participants and non-participants, a task possibly made easierby linking surveys of participants with reflects externalitiesassociated with the impact of subsidizedinterest rates on interest rates in othersectors, as well as the degree to whichclients of subsidized programs get ain the sensitivity of equilibrium credit. The sort ofare at play here, as well as the sensitiv-ity of credit demand to the interestratewill reducing subsidies makecredit too costly for borrowers? Thewith household surveys that have infor-mation on the availability and terms ofthose surveys to information on the tim-ing and scope of new microfinance pro-grams. Those same household surveyscould also be used to measure the sensi-tivity of credit demand to interest rates.Selection problems are notorious inthese kinds of studies, but instrumentalvariables like inherited assets have been reflects theinteraction of average returns, produc-tion technologies, risk, and capitalcosts. Will increased interest rates pushborrowers toward riskier but more prof-librium credit demand and thus limitage returns)? Do better-off householdshave projects with higher returns thanthrough estimates of profit functions,again with an eye to the responsivenessDebates about microfinance subsidi-zation have often been stymied by dif-ferences of opinion about the levels offlat distribution of social weights , positive impacts of inter-, very low re-turns to investments by poorer house-holds, and negative externalities ofsubsidization, on the other hand, tendto put much greater social weight onconsumption by the poor, assume highlysensitive credit demand to interestrates, low impacts or perhaps negativeimpacts of interest rates on returns,moderately high (but not extremelyhouseholds, and small or beneficialDespite the lack of evidence, experi-debate hold their views strongly. Dis-cussion about the role of microfinancemated early in the game, with assertionschecked by counter-assertions and noimmediate route to resolution. Fortu-ments, these are all issues that can beresolved by fairly straightforward em-pirical studies. It is the peculiar circum-stance of the microfinance policy con-textwith donors eager to spend on1596 new programs and ample funds avail-able for subsidizationthat has pre-vented further progress in getting to Social and Economic ImpactsIn principle, self-employment activi-ties started due to microfinance partici-pation can affect households in manyways (if, indeed, that is what house-there should be an income effect, push-ing up consumption levels and, holdingall else the same, increasing the de-mand for children, childrens education,and leisure. But there will also be ef-fects on the value of time, yielding a va-riety of counterbalancing effects. Withmore children becomes costlier, push-ing fertility rates downward. The needto have children help at home (to com-ents) could decrease schooling levels,and, most obviously, leisure may fall ifopportunity costs are sufficiently in-creased. On top of these forces, manyprograms directly advocate family plan-ning and stress the importance ofbring shifts in attitudes, as well as shiftsin the relative bargaining positions ofhusbands and wives. Thus, while con-crease, it is not clear what willMoreover, the extent of net impactsdepends on the opportunities open tohouseholds in the absence of microfi-nance. Households that do not partici-pate in microfinance programs mayhave access to a wide range of informalfinancial mechanisms and other servicesmade for microfinance were made forand for Head Start in the U.S.thatthey could ultimately pay for them-changes in the lives of poor households.partisan support from the outset. HeadStart, which aims to help 3 5 year-oldchildren with disadvantaged back-grounds get an extra leg up on earlygeneral. For African-American chil-dren, however, it has been largely inef-minishing over time (Janet Currie andDuncan Thomas 1995). Publicly-fundedjob training has also had real successes,intensive programs have been expensiveand seldom justify their costs (RobertLalonde 1995). These mixed reports donot overshadow the argument that bothprograms have played important rolesfor many beneficiaries, but they suggestthat marginal dollars would have beenmore effectively used by alternativeAs noted above, microfinance pro-grams have yet to receive that kind ofscrutiny. Visits to areas served by mi-be seen in books of accountsearningsfrom microfinance participation arefor children, new savings accounts, andnew businesses. But are these changesSimple measures can be deceiving.For example, a recent survey shows thatof eligible households that do not bor-row. The difference is sharp, but doesGrameen attract households with Morduch: The Microfinance Promise for education, or is this dif- of the program? A dif-pooling information on all children invillages served by the Grameen Bank.rate for sons from a random sample ofall eligible households is 46 percent(combining those that borrow and thosethat do not). But the fraction is 48percent in a random sample of compa-rable households in control villageswithout program access. Assuming thatcontrol and treatment groups arecomparable, the Grameen educationestimates of the net impacts of pro-grams. The failures which dot the mi-overlooked, overshadowed by the impres-sive claims that arise from successfulWhy the lack of sound statistical evalu-ations? First, many donors and practitio-ners argue that as long as programs covercosts and appear to serve poor house-holds, serious evaluations are a waste ofning the programs themselves. But asthe simple education example aboveMoreover, almost no programs are cov-ering costs. Second, sound evaluationsMany evaluations, not surprisingly,stress the banking side. As above, theevaluations generally measure perfor-mance by on-time repayment rates andthe ability to generate revenues which More recently, evalu-outreachthe number of borrowers be-low official poverty lines, the gender ofborrowers, and the average size of loans Bulletin 1998; Robert Peck ChristenBut nothing is ever truly simple.When money is fungible within theent activities and assets, the impacton women and saving cannot be gaugedwithout taking into account realloca-tions between men and women and be-tween multiple forms of saving and in-vestment. For example, although 95percent of Grameen borrowers are fe-male, Goetz and Sen Gupta (1995) findthat in just 37 percent of cases do fe-male borrowers from Grameen Bank re-tain significant control over loan usehowever, find control is retained in 63suesas well as selection biasrequiresevaluations with carefully constructed Selectiontheir mechanisms ensure that borrowersare more entrepreneurial, better con-nected, more dedicated, and less riskythan non-participants. This success inscreening applicants makes addressingselection biases due to non-random par-The biases can be large. In evaluatingthe Grameen Bank, Signe-Mary McKer-nan (1996, p. 31) finds that not control-ling for selection bias can lead to over- Comparisons are from Morduch (1998) andacre. The Grameen advantage remains elusiveeven after controlling for child-specific, house-hold-specific, and village-specific variables. Pittand Khandker (1998a), however, find some posi-tive effects on male schooleconometric model to estimate parameters with See, e.g., Richard Patten and Donald1598 Grameen Bank has had in identifyingand targeting good clients. It also meansthat every dollar lent by Grameen maybe responsible for as little as half of theSelection bias may also go in the op-posite direction. Many microfinance in-holds. Pitt and Khandker (1998a), forexample, find that poorer householdsare more likely to be Grameen borrow-ers than their neighbors, conditional onvillage of residence and other observ-able characteristics. In cross-sectionalstudies, this outreach can lead to adownward bias on the estimated effect ofcredit on earnings. At the extreme, theeffective targeting of poor householdscan yield the impression that participa-tion in the program makes clients Addressing the selection bias revealsThe second important source of biasis non-random program placement.Many programs are set up specificallyto serve the under-served. Thus, theyweak financial service. This may lead toapparent negative impacts relative tocomplementary infrastructure (von Pis-chke 1991, pp. 305 306), biasing esti-mates upward. The size and signs of thebiases are likely to change as programs The ap-proach has been popular because datacollection is simple, with recall dataoften used in the absence of a baselinesurvey, and because it promises to con-trol for both non-random participationand non-random program placement.Even so, it is subject to potential biases(James Heckman and Jeffrey Smithstudies collected in the Hulme andMosley (1996) volumes. The studies of-fer before-after comparisons, as well ascomparisons between participants andcontrol groups (where the controlgroups are often households that havebeen selected for program participationTwo results are striking. Comparisonof the second and final columns of Ta-ble 4 shows that programs that havetainability make larger net impacts onchanges in their borrowers incomes. (Itis not incidental that those programstend to cater to wealthier households.)Table 4 orders the programs in thestudy by their degree of subsidy depen-dence, ranging from 9 percent (fullprofitability) to 1884 percent (dire fi-nancial straits). The ranking is nearlyidentical to that based on the ratio ofparticipant-control comparisons of in-come changes, ranging from 544 per-cent to 117 percent (a negligible neteven within given programs, wealthierhouseholds benefit more than poorerThese results combine to suggest thatmicrofinance programs targeted to poor Microfinance evaluations based on before-after comparisons include Eric Nelson and Bol-nick (1986), Barbara MkNelly and ChatreeWatetip (1993), Craig Churchill (1995), RichardVengroff and Lucy Greevey (1994), and J. R.Macinko et al. (1997) The reliability of methods based on differ-ences is reduced as the time periods get closertogether, reducing temporal variation. Differenc-ing noisy data can also exacerbate measurementerror; in the classical case this leads to attenu-ation bias. Noisy recall may thus bias downward Morduch: The Microfinance Promise households may offer only limited bene-fits. The results have been used to but-cial sustainability is the surest way todeliver the most bang for the buckand that poorer households should beBut observers have too quicklypointed to the apparent dichotomy. Theunresolved empirical issue is whetherthere is often an important group in themiddleneither the destitute nor pettyentrepreneurs able to pay high interestrates. Is the typical middle-rung bor-Given the sharpness of the results,the Hulme-Mosley studies deserve tobe read carefully. Unfortunately, doingso yields as many questions as answers.Corners were cut in the rush to get thetencies slipped by. Key results vary byas much as 40 percent even where the(ostensibly) identical series is presentedin more than one table (e.g., increasesin family income in their tables 4.1 andand 8.1). Even if the calculations wereconsistent, sample sizes are small forsome of the most important studies.The distribution of impacts in Mosleys(1996) BancoSol study, for example,rests on evidence on just 24 borrowers.In addition, the quality of controlIndonesian studies draw on a controlgroup with fewer women and less accessto formal financial services than theoverall borrower group (p. 55). Table 4shows that the average control group in-come for BRI is 40 percent lower thanfor the borrower groupand the Banco-Sol control group has income the level of the borrower group. Even ifgether, one is left to wonder why some TABLE 4MPACT ANDNDICATORS dependenceindex Average loan , Indonesia2,400,000 924600BKK, Indonesia499,000325538Two RRBs, India25,000106999BancoSol, Bolivia45,00013574322TRDEP, Bangladesh25,00019938KREP Juhudi, Kenya2,4002175172Nine PTCCSs, Sri Lanka700,0002265050SACA, Malawi400,0003982870BRAC, Bangladesh650,00040875Mudzi Foundation, Malawi22318848257KIE-ISP, Kenya1,70023 Data are from Hulme and Mosely (1996), volume 1, tables 3.3, 3.7, 4.1, and 5.1. The final four columnspertain to case studies. AbbreviationsBancoSol: Banco Solidario. BRI: Bank Rakyat Indonesia. BRAC:Bangladesh Rural Advancement Committee. BKK: Badan Kredit Kecamatan. PTCCS: Primary Thrift and CreditCooperative Society. RRB: Regional rural banks. KREP: Kenya Rural Enterprise Programme. KIE-ISP: KenyaIndustrial Estates-Informal Sector Programme. SACA: Smallholder Agricultural Credit Administration. The subsidydependence index gives the percentage increase in the interest rate required if the program is to exist withoutsubsidies; negative numbers indicate profitability without subsidies (Yaron 1992).1600 the control groups had yet to receiveloans. Selection bias associated withselection bias associated with growthhypotheses are provocative, but policydecisions should wait for more careful The Search for InstrumentsMore careful work would be helpedby the availability of instrumental vari-ables. The search for convincing instru-mental variables for credit has yieldedlittle, however. The problem is com-pounded since variables that may be un-related in more developed economiessuch as the structure of production andconsumptionmay be integrally linkeddue to non-separabilities driven by im-perfect and incomplete markets (Mor-duch 1995). It then becomes less likelythat a production-side variable thatexplains credit use does not also helpThe interest rate is a potential identi-fying variable, but since achieving uni-rates are unlikely to vary within a givensible without some variation. Even if in-terest rates vary, it is likely that theobserved attributes of the borrower, un-dermining their use as instruments (PittOther likely identifying variables arethose which affect the supply of creditbut not demand. Zeller et al. (1996, p.proxies for social capital, lender char-acteristics, and program eligibility re-work as long as the community-level TABLE 4 Average annual% change in , Indonesia1722107420.7544BKK, Indonesia7025705.2216Two RRBs, India50549646.0191BancoSol, Bolivia3028112128.1193TRDEP, Bangladesh113881638.7126KREP Juhudi, Kenya175613071.5133Nine PTCCSs, Sri Lanka130198115.6157SACA, Malawi8302762.8175BRAC, Bangladesh51755219.8143Mudzi Foundation, Malawi6656691.4117KIE-ISP, Kenya280717590.5125 Morduch: The Microfinance Promise variables and social capital do not di-rectly affect profitability, investment,etc. This is a high hurdle for socialLender characteristics have appeal.Like community-level variables, though,they will be wiped out when using vil-lage-level fixed-effects methods if thereis no variation in program access withina village. When there is within-villagevariation in program access, however,rules determining eligibility can be thebasis of an identification strategy, a tacktaken by Pitt and Khandker (1998a,b).on simple methods with small samples.In contrast, a series of recent papers onprograms in Bangladesh exploit a sam-ple of 1800 households and carefully(Pitt and Khandker 1998a,b; Pitt et al.1999; McKernan 1996; Morduch 1998).Bank, Bangladesh Rural Advancementdesh Rural Development Board (BRDB).All use a Grameen-style lending modeland nominally restrict access to house-holds holding under half an acre ofsamples from villages with no access toprograms, and the approach exploitsprogram rules that bar wealthier house-holds from participating. These two fea-tures form the core of the quasi-experi-ment, offering two types of controlgroups. The main constraint is restric-tion to cross-sectional information onThe range of questions asked in thesestudies is ambitious, and answering thetion and a series of identifying assump-tions tied to the structure of the econo-metric models. The basic insight issimple, however. The fact that programrules restrict participation to house-holds owning a half acre of land or lessfor identification. A natural first cut atcomparison of outcomes of householdsclustered just below the cut-off line tothose just above, a standard application ofregression discontinuity design (Donaldsteps ahead. First, rather than usingtively a series of household charac-teristics interacted with an indicatorvariable for whether each householdboth lives in a program village and isdeemed eligible to borrow. Identifica-tion thus comes from differences in theway that age, education, etc. affect out-their effects for the eligible subsamplewith program access. Any differencesare assigned to program participation.sumption that there are not importantnon-linearities in the ways that age,education, and the other variablesby gender and by each of the three Pitt and Khandker (1998a) explain outcomesside variables, with the exception of land holdingsand program credit which are in logs. The lefthand side consumption and laare in logs. Pitt and Khandker demonstrate thattheir results are robust to allowing flexibility in thespecification for the land holdings variable but donot show results with flexible treatments of other1602 programs. Concern with gender is moti-vated by the observation that womentend to be more reliable borrowers thanmen (section 3.3 above) and thatwomen may allocate resources differ-Wood and Iffath Sharif 1997; Goetz andSen Gupta 1995). The question is im-portant both for improving program im-pact and for helping better understandA more complicated selection prob-about whether a member of the house-hold should participate but also specifi- in the household should par-ticipate. Pitt and Khandker exploit thefact that credit groups are never mixedby gender (by regulation), and not allThus, men in villages with no malegroups will not be eligible to borrow;surveyed, 10 have no female groups and22 have no male groups (and 40 havegroups). Identification now comes fromcomparing how the roles of age, educa-tion, etc. for men with access to malegroups compare to their roles for menwithout access; likewise for the charac-Of course, the fact that a man is in avillage with no male groups may saysomething about the unobserved quali-ties of the men and the strength of theirpeer networks in that village. If, for ex-ample, the men are poor credit risks,the evaluation will overstate the pureimpact on men who do participate.Similarly, if having a strong peer groupPitt and Khandker partly address thefixed effects, thus sweeping out anyunobserved village-level heterogeneity(estimating with fixed effects withoutis made possible by the fact that a frac-tion of residents in each village is ineli-gible to borrow from the programsdo not control for features of peernetworksor other relevant charac-teristicsthat are specific just to households in program villages. The vil-lage-level fixed effects will only controlfor those unobservables that affect allment thus remains an issue if, as isplausible, the functionally landless arenoticeably different from their wealth-ier neighbors (noticeable to bank staffprograms take this into account whenthe use of a first stage Tobit to explaincredit demand. The Tobit requires thatsecond stage impacts must be assumedliterature, but one that researchers arekeen to relax (Joshua Angrist, GuidoImbens, and Donald Rubin 1996). Italso implies, for example, that marginaland average impacts are equated. Theassumption poses difficulties if the dis-tribution of returns is anything like thatpredict that in any given cohort roughly25 percent show spectacular gains toborrowing, 60 65 percent stay about thesame, and 10 15 percent go bankruptEntertaining these assumptions, how-ever, offers the chance to estimate im-be impossible. The structure cleverly Morduch: The Microfinance Promise exploits the eligibility rules that barlending to households owning over halfan acre of land. Coupled with the often-cited observation that land markets arevery thin in South Asia, there is the ba-exogenous and that is associated withThese identifying assumptions do nothold up in the data, however. Mostcritically, leakages are evident whenlooking more closely at what the pro-grams do, rather than just at what theysay. Hassan Zaman (1997), for example,finds that 28 percent of borrowers fromBRAC are above the half acre cut-off,meen (30 percent) using the data col-lected by Pitt and Khandker (Morduchaverage land holdings are 1.5 acres forGrameen but are over the half acre lineand some borrowers hold over fiveacres. Consequently, nonparametric re-gression yields no obvious discontinuityin the probability of borrowing forhouseholds across the relevant range oflandholdings. Contrary to the evidencefor India, the data also show consider-able activity in the land market, withpurchases during their tenure with thetions about identification, are striking.Pitt and Khandker (1998a) estimate that100 taka lent to a man. Lending towomen has little effect on labor supply,but men take more leisureexplainingpart of the shortfall in consumption in-creases. Conversely, non-land assets in-crease substantially when borrowing isby women, but not by men. Results onschooling are mixed. Schooling of boysis increased whether men or womenborrow. When women borrow fromcreases, but it does not do so whengrams. This may suggest that girls arecalled upon to help take care of workthat their mothers had done prior toPitt and Khandker (1998a) interprettheir finding that loans to women havehigher marginal impacts than loans tomen as an indication of a lack of fungi-bility of capital and income within thehousehold. But since loans to males arelarger on average, in principle the pat-terns of impacts on consumption canal. (1999) find that Grameen participa-tion by women had no effect on contra- positive effecton fertility. Participation by men, how-increased contraceptive use. The mixedfindings should perhaps not be surpris-ing, given the treatment-control set-up.decline in the 1970s and 1980s, so con-trol villages were also in the process of In choosing control groups, the survey strictlyfollows the half-acre cut-off rule. But the Gra-meen Banks eligibility requirement is in fact halfan acre of land or total net assets under the valueof one acre of single cropped, non-irrigated land(Hatch and Frederick 1998). Some householdswith over half an acre may still qualify under thesecond criterion, but mistargeting is so extensivethat considerable leakage remains even under the When calculated conditional on borrowingaverage from Grameen (15,797 taka versus 14,128taka). For BRAC, males cumulatively borrowed5,842 taka versus 4,711 taka for women, and forBRDB, males borrowed 6020 taka versus 41181604 and D. Narayana 1996). Hashemi,effects of program participation on con-traceptive use in a sample of 1300women, but they do not control forframework to consider impacts on sea-sonality, taking advantage of data on la-bor supply and consumption followingthe three main rice seasons. Microfi-nance borrowing is shown to improveacross seasons, and entry into the pro-McKernan (1996) builds on the Pittand Khandker research to investigatenon-credit impacts of the microfinanceprograms in Bangladesh. The questionis important since the programs putconsiderable energy into vocationaltraining and education about health andsocial issues. Beyond these direct so-cial development programs, participa-tion can also provide borrowers withdiscipline, a sense of empowerment,and shared information. Focusing juston credit misses these potentiallyMcKernan investigates these aspectsby estimating the determinants of self-ticipation in Grameen Bank is associ-ated with a 126 percent increase in self-employment profits beyond the directimpact of the capital. Thus, on averageemployment earnings (bearing in mind,though, that self-employment activitiesstart at a low base and are for mosthouseholds a minor share of total in-come). McKernan also finds that non-credit impacts alone raise profits by 50 gether, she argues that the provision ofcredit alone explains roughly half of theaverage increases in self-employmentIdentification here is complicated bythe fact that both microfinance partici-The quasi-experiment is used to iden-struments for capital are the numbersborrowers. The latter instruments aremotivated by the suggestion that havingmore land-owning relatives is likely as-sociated with having greater access tointerhousehold transfers, a commoncredit substitute. However, the instru-ments prove invalid if, as is likely, prof-its are affected directly by the businessconnections, implicit insurance, andfamily responsibilities associated withthe size and characteristics of onesthe identifying assumptions, I take an-other look at the data, focusing insteadfor household- and village-level charac-gender (Morduch 1998). The resultsKhandker studies. After limiting sam-no increase in consumption or educa-tion (and a slight increase in labor sup-ply) when using the data to measure theimpact of program access. For example,have per capita consumption levels that Under the maintained assumptions, Mada-jewicz (1997) finds that when disaggregating capi-tal types, the non-credit impact loses statisticalflect roles of specific types of capital use (in thiscase, greater use of working capital by programparticipants), rather than factors like education or Morduch: The Microfinance Promise ble control groups, a finding that is ro-observables (although the latter resultis not significantly different from zero).The weak findings are consistent withthe presence of a rich variety of alterna-tive institutions available to non-partici-pants: the programs may make impor-tant absolute differences in the lives ofparticipants, even if they have madeBut like Pitt and Khandker (1998b), Ifind some signs of consumption smooth-traced to increased smoothing of laboracross seasons. Taken together, the evi-Khandker have set out an important re-be much less clean than it appeared atfirst, and that using village-level fixedeffects is not a panacea for addressingbias due to non-random program place-that benefits from risk reduction maybe as important (or more important)than direct impacts on average levels ofmixed results show that much morework is required to establish the casefor strong microfinance benefits in this SavingsOne additional means for promotinghousehold welfare is the developmentof facilities for safe but liquid savingsdeposits. Early microfinance programsand showed little interest in doing so.holds were too poor to save. But recentif given appealing interest rates, a con-veniently located facility, and flexibleaccountswith bankers in IndonesiaA recent study of the expansion of ru-ral banking in Mexico shows this possi-bility clearly. Fernando Aportela (1998)measures the impact on savings rates ofthe expansion of Pahnal, a Mexican sav-ings institute targeted to low-incomeclients. Pahnal expanded rapidly in theend of 1993, setting up branches in postoffices, a model that follows the postalsavings programs of Japan and Ger-many. Pahnal also introduced simplerwere offered by earlier programs. Ex-ploiting the fact that Pahnals expansionwas not uniform across regions,ences framework to estimate impacts,finding that expansion of program avail-ability pushed up savings rates by al-most five percentage pointsand by al-most seven percentage points for someBut how much is new savings andassets (and from under mattresses)? Ifportfolio reallocations are substantial,net benefits to depositors may besmaller than it appears at first. Aportelaever, suggesting that much of thecorporating savings mobilization in mi-crofinance programs makes sense for avariety of reasons (Robinson 1995).First, it can provide a relatively inex-pensive source of capital for re-lending.Second, todays depositors may be to-morrows borrowers, so a savings pro-1606 Third, building up savings may offer im-portant advantages to low-income house-holds directly: households can build upassets to use as collateral, they canbuild up a reserve to reduce consump-tion volatility over time, and they maybe able to self-finance investments ratherthan always turning to creditors. Onthe other hand, handling lots of smalldeposit accounts can be prohibitivelying to mobilize savings more aggres-sively, but BRI has made it a major partof their program in Indonesia. A turn-ing point in Indonesia was introductionof the SIMPEDES saving program inhad to save in accounts run by Bank In-donesia that limited withdrawals totwice a month but which offered rea-sonable interest rateshouseholds re-ceived 15 percent on deposits and paidunlimited withdrawals, and this hasturned out to be a boon to risk-aversefully implemented a lottery, such thatchances for prizes increase with theamount on deposit (Mexicos Pahnal haslottery, a system that echoes Britainslong-running lottery-based premiumbonds). These two features have madethe SIMPEDES program very popular,even if interest rates are zero for smalldeposits, 0.75 percent monthly for me-dium deposits, and 1.125 percentmonthly for larger deposits (over about$100) with inflation knocking out muchBy 1988, over four million poorhouseholds were saving through theprogram, and by December 1996, oversixteen million had deposits. Depositsizes are small, with average balances in1996 of $184, suggesting that the aver-off than the average borrower (with anaverage loan balance over $1000). Thisrepresents over $3 billion in savings andgives BRI a relatively cheap source ofhouseholds with means to build up as-sets and better smooth consumption. Asabove, the question is open, though, asto how much is new savings. (Of course,even if the increased savings rates weredue only to simple portfolio realloca-tions, there could still be substantial ef-scale of intermediation and enhancingLike many programs, Grameen didnot focus on mobilizing voluntary sav-ings until recently. The bank now pro-vides opportunities for voluntary sav-ings, but total deposits remain small. Incontrast, SafeSave, an innovative newprogram in Bangladesh, has made vol-untary saving the core of its program.Staff solicit savings from members on adaily basis with the aim to help house-holds convert their ability to save inregular but small amounts into a useful(less flexibly) through participation ininformal rotating savings and credit as-the program was founded in part by Ra-beya Islam, a housewife in Dhaka whohad long experience running ROSCAs.By the end of 1998, SafeSave had over2000 clients, and it appears to havesustainable, although it remains smallPart of the reason that subsidized pro-grams have not been more aggressive in In the U.S., however, banks find that servic-ing a $500 deposit balance can cost as much as $7stantially through encouraging emerging technolo-automatic teller machines (that might possibly be Morduch: The Microfinance Promise rate spreads. Part of the trap that manyearly programs fell into involved banks on loans andpaying depositors a rate to avoid further losses. Since was kept artificially low in the name of was often kepteven lower, and incentives for saving werediminished. The spread ( been the focus of those interested insavings mobilization. Increasing lendingBut this is not the appropriate spreadto maximize if capital is subsidized andthe objective is to enhance welfare in acost-effective manner. A more appro- is the rate at which donors ob- reflects the per unitadministrative costs of managing andgives the donors opportunity cost of + ) gives the pro-in the mid-1990s the Grameen Bankobtained funds from the BangladeshBank at just 5 6 percent while alterna-tive sources of funds would have cost12 15 percent. If Grameen could havemobilized savings at a cost below theBangladesh Banks opportunity cost offunds, the social cost of subsidizationSavings mobilization at deposit ratesabove lending rates can reduce thecosts of programs, rather than add tothemif donors reward microfinanceprograms for generating funds at costsis to split the difference between do-nors and programs of ( + per dollar of savings mobilized and re- is the concessional inter-est rate that subsidized microfinanceprograms pay for capital)and to re-duce concessional lending by donors bydized credit schemes, everyone lost outmenting the proposed scheme, how-and donors can share benefits fromPromoting saving will not alwaysbenefit clients, however. Most impor-tant, rapid inflation can quickly erodethe holdings of poor households (whilebenefiting those holding debt). How-ever, even if individual households findit impossible to adequately protectthemselves, the bank can invest in ap-propriate foreign currencies and assetsto create a hedge. While I know of nomicrofinance institution that has yetdone so, there is no reason not to instraints. Only tightly-regulated institu-tions should be entrusted to hold sav-ings, but this would exclude mostmicrofinance programs (except, for ex-meen, which are chartered banks).Large, traditional commercial banksmay also have cost advantages in han-dling deposits. One answer is that fully-chartered savings banks could operateshould have fully independent accountsand funds, and the savings that are col-lected should in no way be tied to lend-ing operations. However, a contractuallink to exploit the rebate opportunityings bank/microcredit partnership ideaneed further thought before implemen-tation. But both ideas appear sound inprinciple and suggest that there may beportant question for economists. Poor1608 households often appear to be con-strained in their ability to borrow (Mor-duch 1995). This is puzzling, thoughas long as households are not tooimpatient, they should be able to savetheir way out of borrowing constraints(Angus Deaton 1992, section 6.2). Thenew institutions can provide a way to do ConclusionsThe microfinance movement hasmade inroads around the world. In theprocess, poor households are beinggiven hope and the possibility to im-ing poverty alleviation with profits hasmoved far ahead of the evidence, andeven the most fundamental claimsthe movement has demonstrated theimportance of thinking creatively aboutmechanism design, and it is forcingeconomists to rethink much receivedwisdom about the nature of poverty,markets, and institutional innovation. Inthe end, this may prove to be the mostIn particular, the movement hasshown that, despite high transactionscosts and no collateral, in some cases itis possible to lend profitably to low-in-come households. The experiences haveshown as well that many relatively poorhouseholds can save in quantity whengiven attractive saving vehicles; thisborrowing constraints faced by poorconstraints instead of addressing justthe credit side. But the experienceshave also confirmed how difficult it isto create new institutions, even thoselivia, Bangladesh, and Indonesia it tookstrong leadership and special legal ac-commodations. Elsewhere, it has takenlifted the profile of NGOs. While gov-ernment failures become increasinglyevident, NGOs have had the energy,dedication, and financial resources topursue required legislative changes andingly, NGOs can be expected to takemain of state ministries, and interna-tional organizations like the WorldThis is all new, but some receivedwisdom holds. Most important, all elsethe same it remains far more costly tolend small amounts of money to manypeople than to lend large amounts to afew. As a result, the programs arehighly cost-sensitive, and most rely onsubsidies. Initiating a serious discussionabout next steps necessitates first facingup to the exaggerated claims for finan-cial performance that have characterizedIf the movement plans not to aban-don the promise of substantial povertymake hard choices. One avenue is totake another hard look at managementstructures and mechanism design in or-outreach. Doing so will be far from sim-ple, and it is hard to imagine substantialprogress without a second major waveof innovation. Donors can contribute byencouraging further experimentationand evaluation, rather than just replica-of best practices based on existingThe other path is to reopen theconversation on ways that ongoing Morduch: The Microfinance Promise subsidies can benefit both clients andinstitutions. The movement has shownsome successes in coupling efficient op-observers speculate that if subsidies arepulled and costs cannot be reduced, asmany as 95 percent of current programswill eventually have to close shop. Theremaining 5 percent will be drawn fromamong the larger programs, and theywill help fill gaps in financial markets.But, extrapolating from current experi-cially sustainable programs will be lesspoor than those in the typical programbased programs will be the answer fortruly destitute households, but thepromise remains that microfinance mayerably below poverty lines. The tensionis that the scale of lending to this groupis not likely to permit the scale econo-mies available to programs focused onhouseholds just above poverty lines.Subsidizing may yield greater socialThis prospect is exciting, especiallygiven the dearth of appealing alterna-tives, but the promise of microfinanceshould be kept in context. Even in thebest of circumstances, credit from mi-crofinance programs helps fund self-supplement income for borrowersrather than drive fundamental shifts inemployment patterns. 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