Opinion

Micro-finance ban hurts the poor

Susan Thomas | Updated on August 09, 2013

Consumption by the poor has been hit.

While banning MFIs in Andhra Pradesh for malpractices, their services were not understood.



The role of for-profit micro-credit firms in financial inclusion is the subject of controversy. These firms extend credit to households that have traditionally been denied credit by mainstream banking. Access to credit for such households is supposed to alleviate liquidity constraints and improve welfare.

However, there are concerns about whether such credit actually improves the lives of borrowers. Poor people might make bad decisions about credit; they may get trapped in indebtedness. For-profit firms might charge usurious interest rates.

These concerns became particularly prominent when micro-finance firms listed on stock exchanges with very high valuations (e.g. Mexico’s Banco Compartamos in 2007 and India’s SKS Microfinance in 2010). Many people were unhappy when billions were being made out of the task of serving poor people.

Mohammed Yunus, considered the founder of micro-finance, has spoken out against for-profit micro-finance firms. New regulations are being created in developed countries like the US and the UK to better protect the rights of customers of the ‘payday lending schemes’ that are primarily used by the poor. Some governments have engaged in interventions such as interest rate caps or outright bans. These regulations are driven by the desire for consumer protection and improving consumer welfare.

Stringent controls

In December 2010, Andhra Pradesh passed a law that effectively forced the closure of the micro-finance industry by imposing stringent controls on how payments could be collected and how new loans could be originated.

The reason for this harsh intervention was consumer protection: several borrowers were allegedly driven to suicide because they were harassed by agents of micro-finance firms for loan repayments.

As a consequence of the ban, disbursements dropped to a mere 1.7 per cent of loans disbursed prior to the ban. More than Rs7000 crore micro-borrower loans were effectively in default, with recovery rates at 10 per cent.

At the same time that micro-finance came to a standstill in the State, the assets of MFIs elsewhere in the country rose by 25 per cent. The AP government tried to increase the disbursal of loans through self-help groups. However, there was still a shortfall in credit to households, which one estimate placed at about Rs 30 billion(‘Microfinance: the state of the sector report’, 2010, 2011, and ‘The MicroScape, 2012’).

This was an intervention on a large scale: in 2010, AP had a population of 84 million, out of which an estimated 27 million were in households that borrowed from the micro-finance industry. Did this intervention have the desired effect of improving household welfare? Going beyond the impact upon the 27 million persons in borrower households, what was the impact upon the population at large?

In a recent paper titled The real cost of credit constraints: Evidence from micro-finance, Renuka Sane and I ask three questions: Was average household consumption affected when micro-finance was banned? Which households were more affected by the ban? Did the volatility of average consumption change, indicating a higher inability of households to smooth consumption?

Studying the data

We use data from the CMIE ‘Consumer Pyramids’ dataset to answer these questions. CMIE releases average household characteristics collected from a survey of a panel of 1,50,000 households from 200 geographical regions across India.

This data is observed in each quarter from 2008 onwards. We use this to calculate the average household consumption in 14 regions of AP each quarter. We measure the impact of the ban as the difference in the average consumption of AP households in the four quarters before and after the ban. If there are changes visible in AP, these could have been because of the ban or because of other macro-economic developments in India.

Before we can attribute the observed change to the ban in AP, we need to adjust for changes that might have taken place even if there was no ban. For this we ask: What would average household consumption in AP have been if there was no ban? For this, we identify regions outside of AP with similar economic characteristics (such as number of households, average income, education) but which did not implement a ban on micro-finance. We compare the average household consumption between the AP households (where the ban was in effect) and these ‘control’ households outside AP (where there was no ban) during the same periods. The difference between the two can be attributed to the ban.

Decline in consumption

We find that consumption expenditure of households in AP decreased by 19.5 per cent as a consequence of the ban on micro-finance, compared with those outside AP. This decline varied across components of consumption: AP households spent 16 per cent less on food and 34 per cent less on education as a consequence of the ban. Consumption across all income groups was negatively impacted.

The impact was, however, bigger for households with liquidity constraints, such as those in rural regions with access to fewer sources of credit. There is also some evidence of higher volatility in the expenditure on food in AP households after the ban, relative to households in the control regions. This suggests greater difficulties by households to smooth consumption as a consequence of the ban.

Our results thus suggest a fairly large negative impact of the ban on micro-finance. While the ban was initiated by policymakers in AP under the claim that this would help poor people, it has hurt everyone. This analysis has two implications. The first is that even though consumer protection is a noble goal, banning an industry is often not the right way to improve things. Sophisticated thinking on consumer protection is required. The second key idea is that government interventions should be much more rooted in research and evidence. We will blunder from one policy intervention to another, making mistakes along the way, unless interventions are carefully analysed using the data.

The author is Assistant Professor, Indira Gandhi Institute of Development Research, Mumbai

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Published on August 09, 2013
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