Rural financial institutions and microfinance

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<ul><li><p>8/14/2019 Rural financial institutions and microfinance</p><p> 1/17</p><p>KINGSTON UNIVERSITY, LONDONSCHOOL OF ECONOMICS</p><p>Monetary Economics in Developing Countries(FE3178), 2009-2010</p><p>Lecture 3</p><p>Rural financial institutions and microfinance</p><p>Chapter 3, GSF</p><p>1</p></li><li><p>8/14/2019 Rural financial institutions and microfinance</p><p> 2/17</p><p>Introduction</p><p>Banking soundness and financial depth, as well as rural- and</p><p>micro-credit markets, are significant in fostering economic</p><p>growth and development.</p><p>An interesting example illustrating the latter point is</p><p>Bangladeshs Grameen Bankfounded by Muhammad</p><p>Yunus, an economist who won the Nobel Peace Prize for his</p><p>path-breaking scheme.</p><p>That initiative embarked on overcoming the markets failure to</p><p>deliver much needed financial services and pioneered the</p><p>microcredit movement.</p><p>It originally aimed at providing small loans to seemingly risky</p><p>borrowers, and the experiment has resulted in remarkably high</p><p>loan recovery rates.</p><p>1</p></li><li><p>8/14/2019 Rural financial institutions and microfinance</p><p> 3/17</p><p>Problems in underdeveloped markets</p><p>Observing high interest in developing countries rural areas vis-</p><p>-vis urban areas is not uncommon.</p><p>Differences between rates of interest charged within rural areas</p><p>can also diverge significantly.</p><p>For instance, Siamwalla et al (1990, WBER) investigate rural</p><p>credit markets in Thailand. They find that rural sector interest</p><p>rates were in the region of 60%. In contrast, those in the formal</p><p>sector fell within a range of 12-14%.</p><p>The microfinance literature draws from economic models on</p><p>asymmetric information and contract theory.</p><p>Information asymmetries</p><p>1</p></li><li><p>8/14/2019 Rural financial institutions and microfinance</p><p> 4/17</p><p>The key elements for understanding microfinance are</p><p>borrowers lack of suitable collateral and the lack of reliable</p><p>information about those borrowers.</p><p>The first problem leads to moral hazard (involving</p><p>unobservable borrower behaviour), whereas the second is an</p><p>adverse selection problem (there is asymmetric information</p><p>between borrowers and lenders).</p><p>These problems are more generally known as market failures.</p><p>A way of overcoming those obstacles is peer-monitoring, which</p><p>implies thatjoint-liability by a group of borrowers somehow</p><p>helps in enforcing loan repayments.</p><p>Notably, joint-liability also involves peer-selection, and peer-</p><p>pressure if needed, in reaching and complying with a loan</p><p>agreement, respectively.</p><p>1</p></li><li><p>8/14/2019 Rural financial institutions and microfinance</p><p> 5/17</p><p>2</p></li><li><p>8/14/2019 Rural financial institutions and microfinance</p><p> 6/17</p><p>Stiglitz and Weisss (1981) credit rationing model</p><p>Stiglitz and Weiss (1981) make an important theoretical</p><p>contribution to the understanding ofcredit rationing in</p><p>markets with incomplete information.</p><p>Their model is particularly relevant for understanding the</p><p>problems affecting credit markets in developing countries.</p><p>Stiglitz and Weiss show that in equilibrium there may be credit</p><p>rationing, and thus under-investment, in credit markets with</p><p>adverse selection.</p><p>3</p></li><li><p>8/14/2019 Rural financial institutions and microfinance</p><p> 7/17</p><p>The model</p><p>A bank and borrowers populate Stiglitz and Weisss model</p><p>economy.</p><p>Borrowers can invest in one project that lasts for a single period,</p><p>and they need funding equal to L for implementing it.</p><p>In securing that funding borrowers need to provide collateral C</p><p>amounting to less than L.</p><p>The gross payoffs from each project are distributed as F (R, ),</p><p>where R stands for the projects return and measures the</p><p>projects risk.</p><p>Thus successful projects can generate up to R, with higher</p><p>values implying more risk.</p><p>The bank and a borrower agree on a loan equal to L carrying a</p><p>corresponding interest rate r.</p><p>2</p></li><li><p>8/14/2019 Rural financial institutions and microfinance</p><p> 8/17</p><p>A projects failure implies that returns from the project plus the</p><p>collateral are not enough to repay the amount borrowed.</p><p>What a bank ultimately gets back is a maximum amount</p><p>expected to be at least equal to the returns from the project plus</p><p>the collateral, or a maximum equal to the contractually agreed</p><p>sum L(1+r).</p><p>A critical feature of this model is that the interest rate acts as a</p><p>screening device. That is, lenders are able to sort out potential</p><p>borrowers based on the interest rate that they are willing to pay</p><p>for a given loan.</p><p>That is the case because a higher interest rate crowds-out less</p><p>risky borrowers. And that process also increases adverse</p><p>selection problems.</p><p>In turn</p><p>, the mean return on loans -defined as the product of</p><p>the interest rate and the repayment probability- decreases.</p><p>2</p></li><li><p>8/14/2019 Rural financial institutions and microfinance</p><p> 9/17</p><p>As a result even though banks could benefit from charging a</p><p>higher interest rate they may be better-off not doing so because</p><p>increases in that variable triggers adverse selection problems.</p><p>So whether or not banks can actually benefit from a higher</p><p>interest rate will depend on the magnitude of two opposing</p><p>effects.</p><p>One effect arises directly from the higher interest rate and the</p><p>other indirectly from the adverse selection problems.</p><p>So, depending on the net outcome from these forces, beyond a</p><p>point</p><p>r</p><p>~</p><p>lenders may decide on rationing credit.</p><p>That behaviour gives the concave shape to the loans supply</p><p>curve (LS) in the Figure; i.e. a backward bending credit</p><p>supply for high levels of the interest rate.</p><p>2</p></li><li><p>8/14/2019 Rural financial institutions and microfinance</p><p> 10/17</p><p>Note that LS is a function of the mean return on loans</p><p>, and not</p><p>of the interest rate.</p><p>In the Figure LD is the loan demand curve.</p><p>Further,</p><p>r~</p><p>, the bank-optimal interest rate, corresponds to the</p><p>highest possible</p><p>-that is B on the curve linking r and</p><p>- and</p><p>maps to the point at which Stiglitz and Weisss rationing</p><p>equilibrium occurs.</p><p>At that point there is a higher demand than supply for loans, an</p><p>excess demand for loans, which is equal to the distance between</p><p>LD and LS.</p><p>The market clearing interest rate r* corresponds to point A</p><p>where LD and LS. But Stiglitz and Weiss call attention to the fact</p><p>that r* is not an equilibrium interest rate.</p><p>3</p></li><li><p>8/14/2019 Rural financial institutions and microfinance</p><p> 11/17</p><p>That is the case because the repayment probability and thus</p><p>drop sharply in the face of an increasing default risk resulting</p><p>from the higher interest rate. And that leads to a correspondingly</p><p>lower point on the LS curve linked to point A via the 45 degree</p><p>line in the north-west quadrant.</p><p>5</p></li><li><p>8/14/2019 Rural financial institutions and microfinance</p><p> 12/17</p><p>Determination of the market equilibriumStiglitz and Weisss (1981) credit rationing model</p><p>1</p><p>Excess</p><p>demand</p><p>for</p><p>loans</p><p>SL</p><p>SL</p><p>DL</p><p>r</p><p>*r</p></li><li><p>8/14/2019 Rural financial institutions and microfinance</p><p> 13/17</p><p>Overcoming adverse selection problems</p><p>Stiglitz and Weisss (1981)overcoming information</p><p>asymmetry is critical in fostering credit markets in developing</p><p>countries.</p><p>That is, solving adverse selection issues may induce lenders to</p><p>be more forthcoming in facilitating credit to borrowers without</p><p>collateral and traditional banking-customer characteristics.</p><p>But the actual presence of those adverse selection issues may</p><p>explain why in developing countries rural areas informal</p><p>finance, such as that expensively provided by moneylenders,</p><p>prevails.</p><p>Studies by Bell (1990), Siamwalla et al (1990), and Aleem</p><p>(1990), inter alia, actually show that these informal sources</p><p>of finance have been able to coexist with modern financial</p><p>institutions. And that is the case in the face of government</p><p>1</p></li><li><p>8/14/2019 Rural financial institutions and microfinance</p><p> 14/17</p><p>initiatives aimed at fostering a move towards using the latter,</p><p>presumably more efficient, finance option.</p><p>In some cases traditional commercial banking institutions have</p><p>opted to provide services usually reserved to the informal sector.</p><p>What follows explains some theoretical approaches advanced</p><p>with the aim of better understanding key elements making-up</p><p>these fairly successful microfinance initiatives.</p><p>Particularly, explaining the roles of peer-monitoring, and of</p><p>group-lending and joint-liability, has generated an important</p><p>literature on the topic.</p><p>Peer-monitoring and group-lending</p><p>Stiglitzs (1990) is an early contribution to the literature on</p><p>peer-monitoring.</p><p>2</p></li><li><p>8/14/2019 Rural financial institutions and microfinance</p><p> 15/17</p><p>Models based on peer-monitoring argue that -assuming group</p><p>members have better information about themselves than lenders,</p><p>say because they live close to each other- the technology is</p><p>likely to be cheaper than traditional finance monitoring.</p><p>Thusjoint-liability by a group of borrowers is expected to</p><p>somehow help in enforcing loan repayments. Notably, joint-</p><p>liability also involves peer-selection, and peer-pressure if</p><p>needed, in reaching and complying with a loan agreement,</p><p>respectively.</p><p>Varian (1990) also analyses peer-monitoring and joint-liability,</p><p>but he focuses on self-selection issues. Basically, in the model a</p><p>financial institution interviews a member of a given group. And</p><p>based on the outcome from that process the institution decides</p><p>on granting a credit or not. That, in turn, induces what can be</p><p>called a pre-screening exercise by group members before they</p><p>actually apply for credit. I.e., group members self-select each</p><p>other.</p><p>1</p></li><li><p>8/14/2019 Rural financial institutions and microfinance</p><p> 16/17</p><p>Besley and Coate (1995) develop a strategic repayment game</p><p>with joint liability. They highlight the pros and cons implicit in</p><p>this type of scheme. Specifically, they show that successful</p><p>group members may have an incentive for repaying the loans of</p><p>the less successful ones. Yet there are cases in which the whole</p><p>group defaults whereas some members would have paid under</p><p>individual contracting.</p><p>Ghatak and Guinnane (1999) analyse moral hazard problems</p><p>in group-lending. In a model with moral hazard and monitoring</p><p>they find that if the social sanctions are effective enough or</p><p>monitoring costs are low enough, joint-liability lending will</p><p>improve repayment rates through peer-monitoring even when</p><p>monitoring is costly.</p><p>Ghatak (2000) and the related paper by Gangopadyay,</p><p>Ghatak, and Lensik (2005) reach the conclusion that under</p><p>joint liability contracts safe borrowers will cluster and form</p><p>homogeneous groups, while risky borrowers will be screened-</p><p>out.</p><p>2</p></li><li><p>8/14/2019 Rural financial institutions and microfinance</p><p> 17/17</p><p>Ghatak also shows that under individual liability contracts</p><p>adverse selection may lead to underinvestment.</p><p>In contrast, joint-liability schemes can improve efficiency in</p><p>comparison with standard debt contracts.</p><p>3</p></li></ul>


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