rural financial institutions and microfinance

8/14/2019 Rural financial institutions and microfinance

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KINGSTON UNIVERSITY, LONDONSCHOOL OF ECONOMICS

Monetary Economics in Developing Countries(FE3178), 2009-2010

Lecture 3

Rural financial institutions and microfinance

Chapter 3, GSF

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Introduction

Banking soundness and financial depth, as well as rural- and

micro-credit markets, are significant in fostering economic

growth and development.

An interesting example illustrating the latter point is

Bangladeshs Grameen Bankfounded by Muhammad

Yunus, an economist who won the Nobel Peace Prize for his

path-breaking scheme.

That initiative embarked on overcoming the markets failure to

deliver much needed financial services and pioneered the

microcredit movement.

It originally aimed at providing small loans to seemingly risky

borrowers, and the experiment has resulted in remarkably high

loan recovery rates.

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Problems in underdeveloped markets

Observing high interest in developing countries rural areas vis-

-vis urban areas is not uncommon.

Differences between rates of interest charged within rural areas

can also diverge significantly.

For instance, Siamwalla et al (1990, WBER) investigate rural

credit markets in Thailand. They find that rural sector interest

rates were in the region of 60%. In contrast, those in the formal

sector fell within a range of 12-14%.

The microfinance literature draws from economic models on

asymmetric information and contract theory.

Information asymmetries

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The key elements for understanding microfinance are

borrowers lack of suitable collateral and the lack of reliable

information about those borrowers.

The first problem leads to moral hazard (involving

unobservable borrower behaviour), whereas the second is an

adverse selection problem (there is asymmetric information

between borrowers and lenders).

These problems are more generally known as market failures.

A way of overcoming those obstacles is peer-monitoring, which

implies thatjoint-liability by a group of borrowers somehow

helps in enforcing loan repayments.

Notably, joint-liability also involves peer-selection, and peer-

pressure if needed, in reaching and complying with a loan

agreement, respectively.

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Stiglitz and Weisss (1981) credit rationing model

Stiglitz and Weiss (1981) make an important theoretical

contribution to the understanding ofcredit rationing in

markets with incomplete information.

Their model is particularly relevant for understanding the

problems affecting credit markets in developing countries.

Stiglitz and Weiss show that in equilibrium there may be credit

rationing, and thus under-investment, in credit markets with

adverse selection.

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The model

A bank and borrowers populate Stiglitz and Weisss model

economy.

Borrowers can invest in one project that lasts for a single period,

and they need funding equal to L for implementing it.

In securing that funding borrowers need to provide collateral C

amounting to less than L.

The gross payoffs from each project are distributed as F (R, ),

where R stands for the projects return and measures the

projects risk.

Thus successful projects can generate up to R, with higher

values implying more risk.

The bank and a borrower agree on a loan equal to L carrying a

corresponding interest rate r.

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A projects failure implies that returns from the project plus the

collateral are not enough to repay the amount borrowed.

What a bank ultimately gets back is a maximum amount

expected to be at least equal to the returns from the project plus

the collateral, or a maximum equal to the contractually agreed

sum L(1+r).

A critical feature of this model is that the interest rate acts as a

screening device. That is, lenders are able to sort out potential

borrowers based on the interest rate that they are willing to pay

for a given loan.

That is the case because a higher interest rate crowds-out less

risky borrowers. And that process also increases adverse

selection problems.

In turn

, the mean return on loans -defined as the product of

the interest rate and the repayment probability- decreases.

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As a result even though banks could benefit from charging a

higher interest rate they may be better-off not doing so because

increases in that variable triggers adverse selection problems.

So whether or not banks can actually benefit from a higher

interest rate will depend on the magnitude of two opposing

effects.

One effect arises directly from the higher interest rate and the

other indirectly from the adverse selection problems.

So, depending on the net outcome from these forces, beyond a

point

r

~

lenders may decide on rationing credit.

That behaviour gives the concave shape to the loans supply

curve (LS) in the Figure; i.e. a backward bending credit

supply for high levels of the interest rate.

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Note that LS is a function of the mean return on loans

, and not

of the interest rate.

In the Figure LD is the loan demand curve.

Further,

r~

, the bank-optimal interest rate, corresponds to the

highest possible

-that is B on the curve linking r and

- and

maps to the point at which Stiglitz and Weisss rationing

equilibrium occurs.

At that point there is a higher demand than supply for loans, an

excess demand for loans, which is equal to the distance between

LD and LS.

The market clearing interest rate r* corresponds to point A

where LD and LS. But Stiglitz and Weiss call attention to the fact

that r* is not an equilibrium interest rate.

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That is the case because the repayment probability and thus

drop sharply in the face of an increasing default risk resulting

from the higher interest rate. And that leads to a correspondingly

lower point on the LS curve linked to point A via the 45 degree

line in the north-west quadrant.

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Determination of the market equilibriumStiglitz and Weisss (1981) credit rationing model

1

Excess

demand

for

loans

SL

SL

DL

r

*r


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Overcoming adverse selection problems

Stiglitz and Weisss (1981)overcoming information

asymmetry is critical in fostering credit markets in developing

countries.

That is, solving adverse selection issues may induce lenders to

be more forthcoming in facilitating credit to borrowers without

collateral and traditional banking-customer characteristics.

But the actual presence of those adverse selection issues may

explain why in developing countries rural areas informal

finance, such as that expensively provided by moneylenders,

prevails.

Studies by Bell (1990), Siamwalla et al (1990), and Aleem

(1990), inter alia, actually show that these informal sources

of finance have been able to coexist with modern financial

institutions. And that is the case in the face of government

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initiatives aimed at fostering a move towards using the latter,

presumably more efficient, finance option.

In some cases traditional commercial banking institutions have

opted to provide services usually reserved to the informal sector.

What follows explains some theoretical approaches advanced

with the aim of better understanding key elements making-up

these fairly successful microfinance initiatives.

Particularly, explaining the roles of peer-monitoring, and of

group-lending and joint-liability, has generated an important

literature on the topic.

Peer-monitoring and group-lending

Stiglitzs (1990) is an early contribution to the literature on

peer-monitoring.

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Models based on peer-monitoring argue that -assuming group

members have better information about themselves than lenders,

say because they live close to each other- the technology is

likely to be cheaper than traditional finance monitoring.

Thusjoint-liability by a group of borrowers is expected to

somehow help in enforcing loan repayments. Notably, joint-

liability also involves peer-selection, and peer-pressure if

needed, in reaching and complying with a loan agreement,

respectively.

Varian (1990) also analyses peer-monitoring and joint-liability,

but he focuses on self-selection issues. Basically, in the model a

financial institution interviews a member of a given group. And

based on the outcome from that process the institution decides

on granting a credit or not. That, in turn, induces what can be

called a pre-screening exercise by group members before they

actually apply for credit. I.e., group members self-select each

other.

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Besley and Coate (1995) develop a strategic repayment game

with joint liability. They highlight the pros and cons implicit in

this type of scheme. Specifically, they show that successful

group members may have an incentive for repaying the loans of

the less successful ones. Yet there are cases in which the whole

group defaults whereas some members would have paid under

individual contracting.

Ghatak and Guinnane (1999) analyse moral hazard problems

in group-lending. In a model with moral hazard and monitoring

they find that if the social sanctions are effective enough or

monitoring costs are low enough, joint-liability lending will

improve repayment rates through peer-monitoring even when

monitoring is costly.

Ghatak (2000) and the related paper by Gangopadyay,

Ghatak, and Lensik (2005) reach the conclusion that under

joint liability contracts safe borrowers will cluster and form

homogeneous groups, while risky borrowers will be screened-

out.

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Ghatak also shows that under individual liability contracts

adverse selection may lead to underinvestment.

In contrast, joint-liability schemes can improve efficiency in

comparison with standard debt contracts.

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