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

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FEATURE ARTICLESfinding is consistent with a study undertaken amongborrowers in Caja Los Andes, Bolivia, which indicatesthat borrowers with higher non-business incomeare less likely to default on their loan obligations(Vogelgesang: 2003).2. Loan status (OR = 0.1802). The OR implies an82 percent decline in the default rate among newborrowers compared to repeat borrowers. This mayreflect an incentive effect, with access to future loansdependent upon successful repayment of the currentloan. Knowing this, new borrowers prove themselvesto be a good credit risk, a finding consistent withArmendariz and Morduch (2000), Bolton andSharfstein (1990), and Churchill (1999) amongmicrofinance institutions that employ individualliabilityschemes. The result is also consistent withVogelgesang (2003), who shows that loan repaymentrates among repeat borrowers deteriorate comparedto new borrowers.3. Working capital (OR = 0.5126). Loans used forthe purpose of augmenting working capital reducedefault probabilities by 49 percent; this compareswith an increased default rate of 62 percent for loansused to finance fixed investment [see below (8)].4. Guarantors (OR = 0.3732). A unit increase in thenumber of guarantors produces a decline in thedefault rate by 63 percent. This may be due to socialpressures that guarantors bring to bear on recalcitrantborrowers, and may also be seen as social collateralwith its impact on loan repayment. This result isconsistent with Gine and Karlan (2006), who show ina related study that the use of collateral coupled withsocial pressure among borrowers reduces defaultwhile increasing repayment; it is also consistentwith the findings of a study undertaken in Bolivia(Schreiner: 1999).5. Number of years in business (OR = 0.7176). Asthe number of years a borrower has been in businessincreases, the probability of default declines by28 percent. This confirms that as borrowers gaincommercial experience, the resulting improvedproductivity leads to a significant reduction inthe likelihood of default compared to their lessexperienced counterparts. Alternatively, the effectmay indicate that established businesses, withtheir assured revenues and diversified cash flows,represent better credit risks than younger firms.There is considerable evidence that firms withlong operating histories are less prone to financialdistress than are more recently establishedbusinesses.MICROBANKING BULLETIN, Issue 20, September 20106. Collateral to loan ratio (OR = 0.8437). A unitincrease in the collateral demanded by lenders assecurity for the loan lowers the likelihood of default by16 percent, a finding consistent with (Villas-Boas andSchmidt-Mohr: 1999), who argue that as competitionincreases, so too does the demand for additionalcollateral by MFIs. On the other hand, the variable issignificant at only around the 10 percent level.7. Number of dependants (OR = 1.2234). Foreach additional dependant in the household theprobability of loan default increases by about22 percent. As potential claims against businessincome increase, this is likely to encourage thediversion of resources to direct household purposes(paying school fees, funeral pledges, or other socialcommitments).8. Fixed assets (OR = 1.6180). Loans made for thepurpose of acquiring fixed assets increase thelikelihood of default by 62 percent, a result thatappears to connect with the relatively long gestationbefore fixed investments (machinery, plant andbuilding) generate a satisfactory cash flow. Comparedwith loans used for inventory investment, defaultis reduced by 20 percent though this effect was notstatistically significant.9. Monitoring (OR = 1.4786). Monitoring increasesthe likelihood of default by 48 percent. This maybe due to excessive pressure from the institutions’agents encouraging borrowers to invest in highriskprojects in order to generate higher cash flowsto repay the loan. It may also reflect ‘collusion’between loan officers and borrowers; evidence ofsuch behavior is known, or perhaps it may be due tooutright fraud (Todd: 1996).To test the robustness of the estimated coefficientsand by extension the odds ratios, alternative logisticregressions were run that excluded all of thestatistically insignificant variables. Also, variablesthat were closely correlated with each other werealternated to determine which had the greaterability to classify and predict default. Finally, branchdummies were introduced to control for regional orneighborhood effects.The alternative regressions were consistent witheach other and with those estimated using all ofthe independent variables. Nor can any systematicdifferences be detected in the pattern of branchlending, suggesting that screening and creditprocedures were applied consistently and uniformly.By any of the widely used goodness of fit criteria, the12Microfinance Information eXchange, Inc
MICROBANKING BULLETIN, Issue 20, September 2010results are virtually identical; nor do the alternativespecifications materially alter the percentage ofobservations correctly classified.All in all, we conclude that the probability of defaultincreases with the number of dependents, whetherthe proceeds are used to acquire fixed assets, andthe frequency of monitoring, and decreases withthe availability of non-business income, years inbusiness, the number of guarantors, whether theproceeds were used for working capital purposes,and whether the client is a first time borrower. Theratio of collateral-to-loan value is also associatedwith an increase in the repayment rate, though noneof the estimated coefficients are significant at the0.05 level of higher.ReferencesAryeetey (2008): “From Informal Finance to FormalFinance in Sub-Saharan Africa: Lessons from LinkageEfforts,” Paper presented at the High Level Seminaron African Finance for the 21st Century , IMF andJoint Africa Institute, Tunis, Tunisia (March).Armandariz and Morduch (2000): ”MicrofinanceBeyond Group Lending” Economics of Transition, 8(401-420).Bank of Ghana (2007): “A Note on Microfinance inGhana, Research Department Working Paper WP/BOG-07/01 (August).FEATURE ARTICLESBolton and Sharfstein (1990): “A Theory of PredationBased on Agency Problems in Financial Contracting,”American Economic Review. 80 (93-106)Churchill (1999): Client-focused Lending: The Art ofIndividual Lending, (Calmeadow).Gine and Karlan (2007): Group versus IndividualLiability: A Field Experiment in the Philippines, YaleUniversity Economic Growth Centre Working Paper 940(May).Jha, Negi and Warriar (2004): “Ghana: MicrofinanceInvestment Environment Profile,” unpublished ms.,Princeton University.Schreiner (1999): “Scoring Arrears at a Microlender inBolivia,” Journal of Microfinance 6, (70-86).Todd, H. (1996): Cloning Grameen Bank: Replicatinga Poverty Reduction Model in Indonesia, Nepal andVietnam (Intermediate Technology Publications).Villas-Boas and Schmidt-Mohr (1999): “Oligopolywith Asymmetric Information: Differentiation inCredit Markets,” The Rand Journal of Economics. 30(375–396).Vogelgesang (2003): “Microfinance in Times of Crisis:The Effect of Competition, Rising Indebtedness, andEconomic Crisis on Repayment Behavior” WorldDevelopment. 31 (2085-2114).Microfinance Information eXchange, Inc13
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