CGAP has been closely following the issue of over-indebtedness and market saturation of retail credit in several markets (Morocco, Bosnia, Pakistan, India). Most recently, the South African regulator, Gabriel Davel proposed several options for regulators and financial institutions to preempt and prevent credit bubbles. In early 2011, Richard Rosenberg and Jessica Schicks surveyed in great detail the evidence on measuring over-indebtedness, especially from a client perspective, concluding that we are essentially “flying blind” in markets where credit bubbles have not surfaced yet.
Over May and June 2013, CGAP’s blog has been hosting a series on data on market saturation, featuring guest authors who cross compare market level data, as well as research in individual markets like Morocco, and Tamil Nadu in India.
Based on household level data, as well as interviews with MFIs and banks, Isabelle Guerin’s post “Loan defaults versus over-indebtedness in rural Tamil Nadu”, argues that defaulting on loan repayment does not necessarily mean that the client is over-indebted. The author’s research spans a period of ten years, and revisits villages and households. Default can be “strategic”, where a client does not repay because she chooses not to, or cannot repay because of a liquidity crunch. She further argues that payment of loans does not indicate that there is no over-indebtedness, because her data show that MFI loans account for less than 20% of the household debt portfolio. Clients are borrowing from informal sources (often flexible and readily available), as well as SHGs, and consumer credit companies. She does not argue that there is either a repayment problem or an over-indebtedness issue in the market overall.
In reply to Isabelle’s post, the IFMR blog featured a response by Vaibhav Anand who argues, based on solid supply side data, that clients are neither defaulting nor are over-indebted in Tamil Nadu. IFMR Capital, a non-banking finance company with microfinance exposure across India, conducted monitoring visits in Tamil Nadu, covering 7 partner MFIs arguing that their collections percentage was close to 100%. Further, collection efficiency of IFMR Capital’s securitized microloan pools has consistently been high. As of March 31st, their average collection efficiency on 33 active securitized transactions with pools originating from Tamil Nadu was 99.82%. The piece also points out the securitized pools from Tamil Nadu and other states are improving on defaults.
Tamil Nadu has a population of 72 million, with an estimated 13 million people living below the poverty line. Unlike the north, India’s southern states have better and more diverse supply of retail credit, through a wide range of informal and formal institutions. In such a large and complex market, it is probably not surprising that the authors’ methodologies and samples would yield somewhat divergent pictures. But both Isabelle and Vaibhav are trying to get at questions that the global financial services industry is struggling with. Justin Oliver’s piece on multiple borrowing in Andhra Pradesh raised similar issues.
The key question for me in this debate is:
Is repayment or default of a microfinance loan an adequate indicator of over-indebtedness in markets where the range of credit tools used by clients is as diverse and complex as those available in Tamil Nadu?
As Vaibhav points out, recent developments in India, like MFI regulation and development of credit bureaus, have contributed to stronger practices and transparency in MFIs. However, as Isabelle points out in her response, credit bureaus, and I would argue that even household surveys, cannot capture all debt from all possible informal sources.
There is need for more energy and innovation in measuring whole-market penetration and the potential for saturation, both at the market level (through early warning signals), as well as understanding demand and use at the household level through a range of methods.
The next post in this series features a new tool called Microfinance Index of Market Outreach and Saturation, which measures global credit saturation levels across markets.
Also watch this space for: CGAP colleagues Greg Chen and Stuart Rutherford have worked on getting client perspectives in a mature microfinance market like Bangladesh which will be released on the blog this month.
---- Based out of New Delhi, the author is the editor of CGAP’s blog, and manages CGAP’s G2P learning agenda in India.
The real answer to the questions raised can be found only through real authentic and continuous data in respect of the borrowers and their environment which simply does not exist today.The surveys and sample studies cannot give adequate explanation to this multi-dimensional phenomenon with perverse incentives.I propose a model Digital Rural information infrastrtructure and will be happy to privide the details to those intetested.
I am quite interested in the model you are proposing Professor Satch. We had a study done in collaboration with other microfinance investment vehicles in Cambodia. The study tried to assess the objective and subjective definitions of over-indebtedness. The results revealed that multiple loans can be a driver of over-indebtedness.
Dear Shweta Banerjee
Interesting summation. I would like to share three points
First, defaulting repayment phenomenon of any type be it liquidity or strategic one , is principally influenced by human behavior contextually depending on the situation in the vulnerability infested households internally and the value system in the society /community externally. This social cultural and political factors causing debt complexities, has not been sensed in the postings and the responses as well except Isabelle’s one.
Second I agree with Isabelle’s assertion “ too often unfortunately, regular rescheduling practices from MFIs allow them to show good repayment performances and hide repayment problems” This glittering performance at MFI/SHG level from supply side perspectives is to get good rating and avail further funds. But DNA for repayment issue basically is in client side which is non transparent . To wit what are sources of funds for repayment from demand side matter seriously often ignored by the researchers and the lending institutions since most of the repayment is likely made from informal debt there by loan liability or credit crunch remains unabated invisibly. This causative factor merits the attention of the researchers to probe further instead of analysising week data from supply side from institutional perspectives and
Third, informal finance as asserted by Isabelle assumes importance in this analysis since from the client’s perspectives at household level, the functioning of credit (money) and its implication on their livelihood in terms of debt burden would be the same regardless of the type of sources of credit be it formal or non formal or informal. This factor influences much on the repayment status. In this regard , neither credit bureau and nor digital rural information infrastructure (prof Satch) in the given profile of poor clients may not be adequate unless some participatory approach and involvement of social capital in the process of assessing informal sources are taken cognizance of for the said purposes