What Caused Mass Defaults in Karnataka, India
In 2009, there were mass defaults in four towns in Karnataka following orders by a local organization called the Anjuman Committee. The Committee banned borrowers of a religious community from continuing contact with MFIs, following complaints from community members about repayment difficulties and culturally inappropriate MFI practices. Since then, some of these towns have witnessed default rates of more than 50%.
While the origins of mass defaults may be rooted in cultural and political considerations, in this post we focus solely on customer behaviour and the lending practices of MFIs that operate in these towns. We draw from a study on clients (jointly conducted with EDA Rural Systems) conducted in 2010, of 800 customers in two mass default towns, Kolar and Ramanagaram and two comparable control towns without mass defaults, Nanjangud and Davanagere.
Most of the defaults were due to the ban but not all
There were three kinds of defaulters. The main self-reported reason is the ban. Ninety-one percent said that they did not want to repay, even though they had the means to do so, because of the Anjuman Committee’s instructions.
However, a few (4%) said that they could not pay because they did not have the money due to various reasons. This implies that trouble was brewing even without the ban, but in the normal course of action group members would have covered for them. Fourteen percent did not want to repay because their group or center members had defaulted (the questionnaire allowed for multiple responses so answers total to more than 100%).
High evidence of repayment stress in the default towns
There are consistently higher indicators of repayment stress in the default towns. Thirty-four percent of the respondents in default towns skipped meals, important expenses, or sold assets to repay compared to 2% in the comparison towns. And 21% of the respondents in the default towns cited that repayment was a burden as compared to 3% in the comparison towns. This shows that many customers had reason to complain to the Anjuman Committee.
What caused repayment distress?
The main factors of repayment distress were discussed in my previous post. Below is a summary:
- High debt levels
- Unexpected drops in income (a common source of employment, the silk industry in Kolar faced a downturn during this time)
- Diluted MFI loan and client monitoring practices, such as post-loan disbursement checks by loan officers and higher use of loans for non-business purposes
- Low numerical literacy which may have been a contributing factor towards customers taking on more debt than they could manage
Strategic default – Did they choose not to repay?
Close to 20% of the defaulters refused to repay to only some of the many MFIs that they had borrowed from. This suggests that many customers chose which MFIs to default to and presumably could have repaid to even more MFIs if they had chosen to, despite the ban. The take away is that default was a strategic choice for many, and was not only due to the ban.
The following two factors can explain this.
(a) Competition decreases incentives to repay
Studies have shown that the entry of new lenders diminishes incentives to repay to existing lenders, especially if there is no centralized system of information. The table below lists percentage of respondents who stated that they are less committed to pay their loans when they see new lenders in town. These questions are important because they examine why customers defaulted. There is correlation between responses to these questions and default.
(b) Joint liability is not always optimal for repayments
Fourteen percent of customers, in this study, did not repay because their group or center members did not repay.
A forthcoming study by Gine, Krishnaswamy and Ponce analyzes defaults by Hindus in mixed religion centers. Even though Hindus were not prevented from repaying by the local organizations, the study found that the likelihood of default and the amount outstanding at maturity for a Hindu in mixed religion centers is higher, when there is a higher percentage of Muslims, who faced the ban, in the center. This effect becomes noticeable when the density of banned defaulters is higher than 6.25% with higher effects when the density of Muslims is higher than 25% of the center.
MFIs typically attempted to enforce joint liability in the mass default towns. Enforcing individual liability rather than joint liability may result in better repayment performance when a critical threshold of a center has already defaulted. This finding has implications on center composition strategies for risk mitigation purposes.
We find no strong evidence that harsh collection practices leads to strategic defaults.
All the indicators discussed above were better in the comparison towns.
We have presented MFI practices and their relevance to the mass defaults in Karnataka. It does appear that the ban is not the only reason for defaults. Instead, there are several reasons including opportunistic choices by customers.
In addition, many observers point out that fast growth, competition between MFIs, and incentive structures of branch staff have lead to some geographical pockets where riskier customers are being sought and operational processes are diluted.
We find correlation between some processes and competition as above. We further find that there are large variances in practices such as lending more than the client’s capacity to absorb and loan monitoring practices between different MFIs. This is a worrisome fall-out of competition, where an MFI with good practices can be adversely affected if another MFI that the customer is also borrowing from has looser practices.
MFIs with their non-collateralized joint liability loans are vulnerable to geo-political risks. If not well-handled, due to the nature of joint liability groups, a few defaults can sometimes quickly snowball into collective defaults. While the reasons for large scale instability are subject to many hypotheses, MFIs have their work cut out to identify and build strong relationships with important local community organizations that could potentially undermine them.
Karuna: Your observations show that the eagerness to pay back a loan strongly depends on group dynamics and on the availability of alternative lenders. Do you think that a centralized database of credit history, jointly administered by all lenders, could address this issue? Not only would such a database allow lenders to make faster and more accurate creditworthiness decisions. It would also provide extra motivation for borrowers to pay back their loans — independently of group dynamics and lender availability.
1) Absolutely. Also, given the usually high repayment rates, at least in some countries, a credit bureau may be useful particularly to alert lenders about a potentially large scale problem, as much as to identify individual delinquent customers. In high competition places, MFIs could at least share anonymized, aggregate PAR in a location so that MFIs operating there can spot potential repayment issues in advance.
2) I also found from the administrative records that a large percentage of customers who defaulted to an MFI one year, went on to join other MFIs and then repay well. Maybe they had genuine repayment problems one year, which a more flexible product or repayment schedule might have addressed or they regretted the default. So, from a financial inclusion perspective, a credit bureau should make attempts to document reasons why a defaulter defaulted, and be judicious about how many years a default is recorded, so that we do not exclude genuine borrowers who need credit for too long.
Karuna: I agree and appreciate the addition you are making in point (2). This shows indeed that a better understanding of the poors’ behavior could help reduce default rates, hence default costs and interest rates. A very interesting study!