Does Microfinance Cause or Reduce Suicides? Policy Recommendations for Reducing Borrower Stress

• Date: 01 March 2015
• DOI: http://doi.org/10.1002/jsc.2004
• Author: Philipp Otto,Arvind Ashta,Saleh Khan
• Published date: 01 March 2015

Strat. Change 24: 165–190 (2015)

Published online in Wiley Online Library

(wileyonlinelibrary.com) DOI: 10.1002/jsc.2004 RESEARCH ARTICLE

Copyright © 2015 John Wiley & Sons, Ltd.

Strategic Change: Brie ngs in Entrepreneurial Finance

Strategic Change

DOI: 10.1002/jsc.2004

Does Micro nance Cause or Reduce Suicides? Policy

Recommendations for Reducing Borrower Stress

1

Arvind Ashta

Burgundy School of Business , Dijon , France

Saleh Khan

International Development Consultant , Geneva , Switzerland

Philipp Otto

European University Viadrina , Frankfurt , Germany

For some time, a spate of suicides by micro nance borrowers has been making

the news ( Hulme, 2000 ; Micro nance Focus , 2010a , b ). Already in 2006, there were

warnings that the pressures of hitting outreach numbers at any cost, pressures

attributed to the governments of Andhra Pradesh and Tamil Nadu, were creating

immense stress on non-governmental organizations (NGOs), who feared their

subsidies would be cut if they did not expand su ciently ( Guerin et al ., 2006 ).

Indeed, Andhra Pradesh (along with Karnataka) is the state with the most satu-

rated micro nance market in India. Does this quick expansion come at a cost?

Microcredit is the lending of small amounts of money to the poor and  nan-

cially excluded, to enable them to increase income and smooth consumption.  e

industry has experienced fast growth rates and has been considered as an important

tool of economic development, in conjunction with other initiatives such as edu-

cation, health, sanitation, infrastructure, and public governance. Based on over

3552 reporting micro nance institutions (MFIs), the Microcredit Summit Cam-

paign indicates that the sector has an outreach of 155 million people in 2007

( Daley-Harris, 2009 ). Table 1 provides details of median MFIs from a sample of

over 1000 MFIs reporting to the Micro nance Information Exchange (MIX).

2

 e median MFI has about 100 employees, who lend to about 10,000 people.

 e average loan size is US$523.  e average MFI lends at about 28% per annum

and makes a positive return on both equity and assets.  e portfolio at risk (30

days) is about 4.76%. In 2006, a Nobel Peace Prize was awarded to an MFI and

its founder to indicate the importance of this initiative. Millions of people are

The links between suicides and

micro nance are stronger for

banks than for MFIs.

Changing roles in the family may

be a cause for stress.

Support groups are required for

males to adapt to this change.

1 JEL classi cation codes: O1, O17, O5.

2 www.mixmarket.org/m /benchmarks , consulted on December 29, 2010.

Male suicides seem to accompany micro nance growth and penetration.

166 Arvind Ashta, Saleh Khan, and Philipp Otto

Copyright © 2015 John Wiley & Sons, Ltd. Strategic Change

DOI: 10.1002/jsc

Putting the two concepts together, if certain micro -

nance practices are now considered dangerous, and if the

chief executive o cers (CEOs) of micro nance  rms take

no action to account for the involved risk, they could be

held to be vicariously strictly liable.

In this context, one can understand that the Andhra

Pradesh government has passed an ordinance ( Andhra

Pradesh government, 2010 ) limiting interest rates at a

maximum of the loan amount and curtailing unethical

collection practices.

As can be seen from Table 1 , the median MFI in

India in 2009 was almost three times larger in terms of

employees than the median worldwide.  e median

Indian MFI has six times more borrowers, thus indicating

that employees also serve more borrowers. Although the

average loan amounts in India are much smaller, only

about US$150, the interest yields are lower at around

24% instead of 28%. Despite this, Indian MFIs have a

higher return on equity.

Why are Indian MFIs more e cient than global

MFIs? Essentially, this is the result of the peculiarity of

the Indian model, which is based on self-help groups

(SHGs) and joint liability groups (JLGs). It is estimated

that 58% of Indian micro nance outstanding loan port-

working in this sector, often as volunteers or at lower than

market wages and returns because they believe that micro-

 nance (as opposed to handouts) can create a di erence

at the grassroots level by making people responsible. Now

one wonders whether the initiative has gone awry.

Recently, Micro nance Focus ( 2010a ) has reported

that there have been 54 suicides by micro nance borrow-

ers in the state of Andhra Pradesh alone. When there are

one or two suicides it could be chance or coincidence, but

adding up to 54 brings the question of who can be liable.

 e English law of torts distinguishes between strict

liability and vicarious liability . Strict liability is liability

that does not depend on actual negligence or intent to

harm, but is based on the breach of an absolute duty to

make something safe.  e question here is, when does

micro nance become dangerous? Which practices are to

be avoided? What features are required to make micro-

loans safe? Who is responsible for safety?  is brings us

to the concept of vicarious liability (the etymology of

which is that the vicar is an agent through whom the Pope

exercises authority).  is is liability that a supervisory

party (such as an employer) bears because of the action-

able conduct of a subordinate or associate (such as an

employee).

Table 1. Characteristics of a Median MFI institution (2009)

Institutional characteristics All India

Number of MFIs reporting 1132 to 1146 * 88

Personnel 104 287

Number of MFIs 1146 88

Number of active borrowers 10,487 65,008

Percent of women borrowers 63.39% 100.00%

Gross loan portfolio USD 4,902,526 8,405,779

Average loan balance per borrower USD 523 144

Number of MFIs 1141 88

Return on assets 1.50% 1.81%

Return on equity 7.27% 10.54%

Yield on gross portfolio (nominal) 27.93% 23.97%

Portfolio at risk > 30 days 4.76% 0.51%

Source : http://www.mixmarket.org/m /benchmarks/csv .

*All 1146 did not provide information for all indicators.