THE NATURE OF MICROFINANCE PRODUCTS AND THEIR IMPACT ON CUSTOMER LOYALTY IN MICROFINANCE
Designing optimal incentives for credit agents has become a major concern for microfinance institutions (MFIs). The existing literature on microfinance has focused almost exclusively on incentive issues between borrowers and lenders. Yet, as credit agents play a critical role in ensuring the success of the MFIs they are working for, they must also be provided with adequate incentives. Without those, they may exert insufficient effort to learn about potential borrowers’ characteristics, they may under-report repayments, and they may mis-represent the information they have obtained on potential borrowers. The issue is particularly acute in pro-poor MFIs.
Many pro-poor MFIs derive from non-governmental organizations and have non-profit status. Their objective is to give access to credit to very poor individuals with viable projects. There has, however, been a widely noted ‘mission drift’ among these MFIs as they increasingly tend to work with clients that are less poor, a drift that has accelerated with rising competition from for-profit MFIs (Weiss and Montgomery, 2005). This has led donors supporting these institutions in search for mechanisms that can induce MFIs to resist mission drift. This paper explores how to design credit agent incentives to achieve this result.
If less-poor borrowers reimburse more on average than very poor borrowers, a pro-poor MFI must strike a balance between financial viability and the selection of very poor borrowers. It wants to lend to an optimal mix of very poor and less-poor borrowers selected among individuals with high ability and most likely to repay. The higher expected repayment rate with less-poor borrowers is used to cross-subsidize loans to very poor individuals, while meeting the zero profit constraint. But incentives for credit agents to select high-ability individuals then tend to conflict with incentives to select very poor individuals, who repay less. For these MFIs, repayment is an incomplete measure of an agent’s performance.
To induce search for information on simultaneously poverty levels and ability, one would need to use incentive schemes that are non-monotonous in repayment. But such schemes would give the agent incentives to hide, and possibly withhold, repayment. Observing another variable correlated with wealth is thus necessary. This requires costly audits. To meet their objectives, pro-poor MFIs thus have to bear the additional cost of an audit generating signals on the true type of borrowers; by contrast to for-profit MFIs that bear no cost in providing incentives to their credit agents. Audits are, however, not always feasible. In contexts where the cost of finding information on wealth is high, a pro-poor MFI will choose to offer incentives based only on repayment performance, as would a for-profit MFI.
An important assumption in our analysis is that wealth and repayment are positively correlated. If less poor borrowers were not more profitable to the lender than the very poor, there fundamentally would be no tension between the outreach objective of the MFI and the viability constraint. There would be no possibility for cross subsidization, and lending to the poor would have to be viable. This would lead to the particular case in which a pro-poor MFI would always lend only to very poor borrowers, and only to very poor borrowers of high ability if acquiring information on ability is not too costly.1 Yet a positive correlation is more plausible2, at least in areas where mission drift is a concern. A positive correlation arises when less poor borrowers have access to better education, better quality inputs and land, and better social capital that facilitate success in their activity. Empirically, Sharma and Zeller (1997) in Bangladesh, SEF (2003) in South Africa, and Zeller (1998) in Madagascar all find that repayment performance does increase with wealth — the very poor tend to invest in low-return activities and in poorly developed markets where environmental and economic shocks are frequent, while they have low ability to bear risk (Hulme, 2000). This positive correlation, and the conflict it creates between outreach and financial viability, is confirmed by a concern among practitioners that using repayment performance incentives for agents reinforces their drift towards less poor clients. Based on a global survey of microfinance institutions, McKim and Hugart (2005) report that 70% of the MFIs that implemented an incentive scheme acknowledged that it reduced focus on the target population.
Another assumption made is that the agent is risk neutral with unlimited liability. This (admittedly special) case is particularly useful as it offers a clear benchmark: a for-profit MFI would bear no incentive cost due to delegation in this setting. Any cost borne by a pro-poor MFI stems exclusively from the interaction of the particular objective of a non-profit organization with internal incentives. If the agent was protected by limited liability or was risk averse, additional incentive costs would obviously arise but our main insights would hold.
References
J. Weiss and H. Montgomery, Great expectations: microfinance and poverty reduction in Asia and Latin America, Oxford Development Studies 33 (3) (2005), pp. 391–416.
M. Sharma and M. Zeller, Repayment performance in group based credit programs in Bangladesh: an empirical analysis, World Development 25 (10) (1997), pp. 1731–1742.
SEF, Small Enterprise Fund, 2004 SEF, Small Enterprise Fund, Overview, 2003 (2004) www.sef.co.za/sef%20overview/more_about_sef.htm.
M. Zeller, Determinants of repayment performance in credit groups: the role of program design, intra-group risk pooling, and social cohesion, Economic Development and Cultural Change 46 (3) (1998), pp. 599–620.
D. Hulme, Is microdebt good for poor people? A note on the dark side of MicroFinance, Small Enterprise Development 11 (1) (2000), pp. 26–28.
A. McKim and M. Hugart, Staff incentive schemes in practice: findings from a global survey of microfinance institutions, MicroFinance Network (2005) http://www.mfnetwork.org/working_groups/staff_incentives.html.
MicroLINKS, 2008 MicroLINKS. 2008.
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