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Over the past several decades, microfinance, broadly defined as financial services to poor and low-income clients, has become an increasingly important tool for governments, multilateral agencies, and nongovernmental organizations (NGOs) to address poverty. Initially, for example, with Banco Sol in Bolivia, the Grameen Bank of Bangladesh, and Bank Rakyat of Indonesia, microfinance was focused primarily on microcredit, small loans to poor people. The basic idea was to extend credit to poor people who do not have access to finance, enabling them to help themselves. In designing products for the poor, the industry has made substantial innovations in the practices used in lending. In addition, some microfinance institutions (MFIs) now offer a range of financial services, including savings vehicles, money transfers, and insurance specifically designed to meet both the needs and specific situations of poor people. Broad recognition of microfinance as a development strategy came with the United Nations (UN) declaring 2005 The International Year of Microcredit and with the awarding of the 2006 Nobel Peace Prize jointly to Mohamed Yunus and to the Grameen Bank, which he founded.
In this research paper, we examine some of the economic questions associated with microfinance, particularly credit.1 At the most basic level is the question of why the poor have not had access to finance in the past. A surprising outcome of the “microfinance revolution,” as it was referred to by Marguerite Robinson (2001), is evidence that poor people, despite their impoverished situation, are good credit risks. Poor people borrowing small amounts of money almost always repay their loans, including sometimes fairly steep interest charges, and do it on time. This suggests that they find productive uses for the funds (“Economics Focus,” 2009). But if they are good credit risks, why haven’t banks been operating in this sector in the past—that is, what is the market failure? To answer this, we look at how banks function as a response to problems of asymmetric information. Over the past several centuries, banks have developed a number of common practices to address these problems, such as the use of collateral, restrictive covenants in binding loan contracts, credit registries, and so on. However, many of them are not applicable to poor people. The innovative practices developed by microfinance institutions serve as alternative, innovative responses to this same problem.
Some Brief General Statistics
Before looking directly at the economic questions of microfinance, it is helpful to look at some statistics from the industry. There has been substantial and sustained growth in the microfinance industry. Between 1997 and 2005, the number of microfinance institutions increased from 618 to 3,133. The number of borrowers increased from 13.5 million to 113.3 million, with 84% of them being women (Daley-Harris, 2006). This works out to an average annual increase of nearly 20%. Gonzalez and Rosenberg (2009) find a lower value when adjustments are made to data sources that incorporate institutions for the first time as if their number of borrowers are all new when in fact the institution has had years of history and borrowers. Still, their adjusted figure of an average growth rate of 12% is substantial, although they do note a recent slowing.
The Gonzalez and Rosenberg (2009) data provide some other interesting results. For example, while microfinance is often associated with NGOs, they show that in 2004, these institutions accounted for only 24% of borrowers (financial cooperatives are not included as there was not sufficient representation in the sample to analyze). Licensed private banks and finance companies accounted for a further 17%, while state-owned institutions accounted for 30%. The remaining 29% was accounted for by the large number of borrowers in Indian self-help groups. As these Indian self-help groups are mostly financed through state banks, adding them to share from state-owned institutions indicates that government-financed organizations account for well over 50% of all borrowers. Note, however, that these statistics exclude financial cooperatives for which there was not sufficient representation in the sample to analyze.
Survey data from Lapenu and Zeller (2001) indicate that only a small percentage of microfinance institutions use the group lending methodology that has been so closely associated with microlending, while most microfinance institutions use individual-based lending. Yet, the 16% of institutions using group lending account for more than two thirds of the actual microfinance borrowers.
Gonzalez and Rosenberg (2009) also report on the profitability, concentration, and geographic distribution of microfinance. Measured by number of borrowers, South Asia dominates with 67 million of the 94 million borrowers in the database. East Asia and the Pacific comprise another 21 million. Sub-Saharan Africa and Latin America comprise 6 and 5 million, respectively. Because of a relatively late start, the regions of the Middle East/North Africa and Eastern Europe/Central Asia have relatively little microfinance. Because advanced economies have wealthier populations and well-developed financial systems, borrowers are a very small percentage of their population.
South Asia is very populous, but even on a per capita basis, it has twice as much microcredit as any other region with nearly 2.25% of the population having microfinance loans. The regions of East Asia and the Pacific and of Latin America have just over 1%, with sub-Saharan Africa slightly lagging.
One of the biggest debates in microfinance in recent years has been the issue of sustainability, the ability of a microfinance organization to maintain its operations without donor funds or subsidization. While most MFIs are not profitable, 44% of borrowers work with profitable MFIs. Interestingly, they find that MFIs tend to be more profitable than the commercial banks operating in the same country, although the data set does not include many tiny MFIs, which would tend not to be profitable. Not surprisingly, profitable MFIs in the database grow faster than those that are not profitable.
Like many industries, especially in developing countries, microfinance tends to be highly concentrated. Within a country, the median share of the largest MFI is one third of the entire market. The median share of the top five microfinance institutions in a country is 81%, and for the top 10 MFIs, it is 95%. This high level of concentration also extends to the world market. Nine percent of the MFIs account for 75% of all microfinance borrowers.
Microfinance as a Response to Information Asymmetries
Asymmetric Information in the Banking Sector
Like many a proverb, there is some wisdom within. To understand why this proverb rings true as a description of the way finance often works, consider a basic problem of finance, information asymmetry. Information asymmetry arises when the agent on one side of a transaction has more information than the agent on the other side and the agent with more information cannot easily and credibly convey that information to the other party even if he or she tries.
While standard examples of markets with asymmetric information include the used car market and markets for insurance, we also find the problem of asymmetric information in finance. Consider one of the basic functions of a financial sector, financial intermediation. Financial intermediation is the process in which an organization gathers funds from those who do not have immediate productive use for it and channels these funds to those who can use it productively. Banks, the most important financial intermediary, take in deposits and then use these funds to make loans to individuals, businesses, and governments. Traditionally, the banks earn income from the interest rate spread (i.e., the difference between the interest paid to their depositors and the interest earned on loans). Like any firm, they seek to ensure revenues cover their costs. Their costs include not only the interest paid on deposits but also the costs of screening, monitoring, and enforcing loan agreements with borrowers; the costs of providing additional services to clients; overhead expenses; and so on. Banks also incur losses from unpaid loans. To deal with the risk of unpaid loans, the interest rates charged to borrowers reflect the level of credit risk (i.e., the risk that the borrower will not pay back the loan).
Consider the two types of credit risk associated with business loans. First, credit risk arises because there is uncertainty about the income the borrower is able to generate from its activities. The borrower may not pay back the loan because of a bad outcome of those activities; for example, demand for a redesigned product may not be as large as expected. The higher the probability of a bad outcome, the higher the probability the borrower will be unable to make its payments and default and the lower the expected income from the loan. Banks charge a higher interest rate to reflect this credit risk.
Second, credit risk stems from asymmetric information because the borrower’s actions are not completely observable. The borrower always knows more about his or her activities than the bank and may engage in behaviors that would negatively affect the bank. For example, suppose Joe the plumber receives a bank loan to purchase a new piece of plumbing equipment that will enable Joe to expand his business and earn greater income. The first type of behavior of concern for the bank is that Joe may simply break the promise to pay back the loan. Another behavior of concern would be if Joe decided not to work as hard once he received the loan. Furthermore, Joe may engage in more risky activities than the bank would desire, for example, by not using the money to purchase new plumbing equipment but instead purchasing a share in a racehorse that his neighbors own. These examples are of behaviors that occur after the loan has been made and are referred to as problems of moral hazard. Once the loan is made, the incentives for the borrower such as Joe to do what he said he would do may no longer be as strong. If the bank cannot monitor the borrower’s behavior, the borrower may, for example, prefer to work less hard since it is now the bank’s money at risk, and he can try to renegotiate the payment schedule of the loan claiming he is unable to pay because of a bad economy beyond his control rather than because he did not work hard. If the problem of moral hazard is great, banks may choose not to lend, and there is market failure from this information asymmetry.
The problem of information asymmetry before the loan occurs can result in adverse selection or the “lemons problem” (Akerlof, 1970). A potential borrower knows more about his or her intentions of keeping a promise than the lender. Because the lender does not know whether a given borrower represents a high risk of breaking its promise, the bank will charge an interest rate that repre-sents the expected level of risk determined by the bank’s assessment of the proportion of high-risk and low-risk applicants for loans. However, those who know they are low-risk borrowers may not be willing to pay such a high interest rate and may drop out of the market. As the low-risk applicants drop out, the probability of a loan applicant being high risk increases, the expected risk is higher, and the bank will charge a higher interest rate. This causes even more low-risk applicants to drop out of the market. Thus, there is “adverse selection”: The low-risk applicants select themselves out of the market, and only high-risk applicants remain.
As banks generally prefer not to lend to high-risk borrowers, at the extreme, they may stop lending. In this case, the market fails (i.e., there is no lending) because low-risk borrowers cannot credibly identify themselves in this situation of asymmetric information. In a less extreme outcome, banks may choose not to charge high interest rates and instead ration credit to try to avoid asymmetric information problems (Stiglitz & Weiss, 1981). Although it is not complete market failure, it still presents problems for access to finance, especially to the poor. If banks are rationing credit, one must ask what determines who receives the limited amount of loans.
Standard Banking Responses to Problems of Asymmetric Information
Over time, banks have developed a number of mechanisms of screening, monitoring, and enforcement to try to mitigate problems of adverse selection and moral hazard. These mechanisms are explored here while the following section describes the alternative mechanisms microfinance has developed specifically for the poor.
To deal with the adverse selection problem, banks become experts both in analyzing information produced by the firm and in the further production of information about firms. This enables them to better assess risk and screen out good risks from bad risks. Established firms seeking a loan provide formal accounting data for the bank to assess. However, banks may gather additional information on the background of potential borrowers to determine, for example, if they had been involved in criminal activities in the past. Banks may also seek to collect information on the credit history of potential borrowers to find out whether they have failed to fulfill past promises or been unable to meet payment obligations such as rent, utility bills, credit card payments, and so on. Banks may also gather information about previous business activities to assess the business acumen of the potential borrower. They may also look at the market in which the potential borrower is operating to better assess the borrower’s business plans and projections of future costs and sales.
Banks also often require collateral, a valuable asset that is surrendered to the bank if the borrower is not able to fulfill its promise to repay the loan (remember the proverb!). Collateral requirements reduce adverse selection as high-risk borrowers who know they are unlikely to pay off the loan are less willing to risk giving up a valuable asset and may choose not to apply. Furthermore, collateral helps to mitigate the costs of adverse selection to the bank when a high-risk borrower defaults. However, even if the bank were to take possession of the collateral, there are likely to be significant costs involved for the bank, and it may not be able to recover the full value of the loan.
To further reduce the other problems of asymmetric information, moral hazard problems, banks engage in monitoring and write debt contracts with restrictive covenants that limit the behavior of borrowers. For example, banks may monitor borrowers by requiring them to regularly report sales volumes, maintain bank accounts at the lending bank, and so forth. They may have a contract that limits how the funds can be used, for example, to purchase a certain type of equipment. These contracts are enforceable through the courts and provide the bank with recourse should the borrower try to use the money for other types of activities that the bank would deem undesirable. Bank loan officers may choose to visit the borrower to check that the funds are being used as agreed. Of course, collateral also serves to reduce the problem of moral hazard. The loss of the valuable asset continues to provide an incentive to borrowers to fulfill their promise to repay.
Note that these responses to the problems of asymmetric information are standard in “arm’s-length” banking systems. An alternative response is “relationship banking,” which is more common in Asian countries. In these banking systems, banks develop strong ties to groups of firms, for example, through cross-ownership. Because of the close ties, banks have more intimate knowledge of borrowers and often some control, lessening the problems of information asymmetry. Both systems have advantages and disadvantages. For example, arm’s-length banking systems do not work well in countries that do not have strong judicial systems to enforce contracts. The relationship banking system may lead to problems of transparency, due to a lack of information production or because the close ties can lead to cronyism and noneconomic decision making. For further discussion, see Rajan and Zingales (1998).
The Microfinance Innovation
While, generally, the poor are considered bad credit risks because they lack net worth that might be used to pay off a loan in the event of a bad outcome of a business activity, perhaps more important is the problem that many of the standard responses to the problems of asymmetric information mentioned above are not appropriate for microloans to the poor, particularly in developing countries. First, the poor lack easily valued, marketable assets that could serve as collateral. Furthermore, especially in developing countries, the poor are less likely to engage in activities that would provide relatively accessible background information about them (credit cards, utility payments, etc.). Their business activities may be more informal and lack financial records or business plans. Many developing countries may also not have a well-functioning legal system to enforce contracts. For example, according to the 2008 Doing Business Report, the time required to settle a contract dispute in India is 1,420 days. This is nearly three times the average of Organisation for Economic Co-operation and Development (OECD) countries and represents a significant cost to any party trying to enforce a contractual obligation.
Microfinance has developed a number of innovative practices that serve as alternative responses to asymmetric information problems. These include group lending, dynamic incentives, regular repayment schedules, collateral substitutes, and lending targeted toward women. These are discussed individually below.
Once every week in villages throughout Bangladesh, groups of forty villagers meet together for half an hour or so, joined by a loan officer from a microfinance organization. The loan officer sits in the front of the group (the “center”) and begins his business. The large group of villagers is subdivided into eight five-person groups, each with its own chairperson, and the eight chairs, in turn, hand over their group’s passbooks to the chairperson of the center, who then passes the books to the loan officer. The loan officer duly records the individual transactions in his ledger, noting weekly installments on loans outstanding, savings deposits, and fees. Quick arithmetic on a calculator ensures that the totals add up correctly, and, if they do not, the loan officer sorts out any discrepancies. Before leaving, he may dispense advice and make arrangements for customers to obtain new loans at the branch office. All of this is done in public, making the process more transparent and letting the villagers know who among them is moving forward and who may be running into difficulties.
This scene is repeated over 70,000 times each week in Bangladesh by members and staff of the Grameen Bank, and versions have been adapted around the world by Grameen-style replicators. Other institutions instead base their methods on the “solidarity group” approach of Bolivia’s BancoSol or the “village bank” approach operated by microlenders in seventy countries through Africa, Latin America, and Asia (including affiliates of FINCA, Pro Mujer, and Freedom from Hunger). For many, this kind of “group lending” has become synonymous with microfinance. (Armendariz de Aghion & Morduch, 2005, p. 85)
In his innovative, seminal venture into microfinance, Mohammed Yunus recognized that the very low income of the potential borrowers he was targeting meant that collateral, the standard tool used in lending in developed banking systems, was not a mechanism he could use to reduce the basic lending problems of adverse selection and moral hazard. Instead, he devised a way to use the social ties among a group of borrowers to help avoid these problems.
In the traditional Grameen model of group lending, loans are administered to groups of five borrowers who form voluntarily. Loans might be used for rice processing, the raising of livestock, traditional craft materials, and so on. The process of lending starts with two members of the group receiving funds. After these two start making regular payments, loans are gradually extended to two additional members and eventually to the fifth member. In the Grameen model, the group meets with their lender weekly, along with seven other groups, so that a total of 40 group members participate. In this way, the program builds a sense of community, or social capital, as well as individual self-reliance. However, just as important is a “joint responsibility” rule in forming the group that if any one member of the group of five defaults, all of the members will be blocked from future access to loans from the lender. Some group lending programs are even more restrictive, requiring “joint liability.” In this type of program, group members may be required to make payments in the case of nonpayment or default by one of its members.
Group lending works to avoid the problem of asymmetric information by taking advantage of local information in screening, monitoring, and enforcement. Group members often know each other and can monitor each other relatively easily. The joint responsibility rule ensures that it is in all group members’ interest that each member meet his or her obligations. The group may also serve as a localized enforcement mechanism, able to threaten social isolation (or even physical retribution). Together, these provide a powerful antidote to moral hazard problems. To ensure obligations are met, the group may also serve as an informal insurance contract so that if one member has a bad outcome, such as becoming sick, the others may make the sick member’s payments until he or she can return to work.
Local knowledge may also help address the problem of adverse selection. Potential borrowers may be able to distinguish who among them are inherently risky borrowers and who are relatively safe borrowers. If banks knew this information, they could charge higher interest rates to the more risky borrowers to reflect the greater risk of default. By allowing groups to form voluntarily, potential borrowers can sort themselves into groups of relatively risky and relatively safe borrowers. Safe borrowers will seek to stick together. Risky borrowers will have no choice but to form groups with other risky borrowers. Because risky borrowers will have more instances of default, the joint liability rules ensure that they make more payments to cover other members of their group when their risky ventures do not succeed. Members thus effectively pay a higher interest rate than the safe borrowers in their separate groups. This sorting helps transfer the risk from the banks to the risky borrowers. And this occurs without the bank itself uncovering the information and without different contracts for different groups (Armendariz de Aghion & Morduch, 2005; Ghatak, 1999; Morduch, 1999).
However, there may be some drawbacks to group lending when social ties are too strong between friends or relatives, worsening repayment rates. Higher levels of social cohesion may lead to collusion among borrowers to cheat the microfinance lender. In addition, group lending may not work as well for larger loan amounts.
A common practice of microfinance institutions is dynamic incentives in extending an initial loan to a borrower for only a small amount but increasing the loan amount over time with successful repayment (Besley, 1995). This is sometimes referred to as “step lending” or “progressive lending.” For the borrower, the increasing access to funding provides an incentive to continue to meet obligations and so reduces moral hazard. Furthermore, the repeated aspect of these transactions establishes a long-term relationship between the borrower and the lender, facilitating the MFI’s gathering of “soft information” about the borrower.
The effectiveness of dynamic incentives is limited when borrowers are mobile and when there are competing lenders in the area. If a borrower changes location, the advantage of the established relationship is lost, and the borrower no longer has an incentive to pay back the loan to gain a larger subsequent loan. Thus, the value of such dynamic lending may be low in more urban areas in which mobility is high and other lenders are available in neighboring areas. Borrowers who default may find it relatively easy to move to another area and start a new relationship with a lender who is unaware of the previous default. In some areas, MFIs are working to share information to reduce this problem.
Regular Repayment Schedules
In developed banking systems, the common practice in small business lending is for payment in full (a lump-sum payment) of principal and interest at the end of the loan period. In contrast, microfinance institutions often issue loans with frequent payment schedules that begin soon after the loan is disbursed. For example, the terms for an annual loan may consist of 50 weekly payments of principal and interest that begin 2 weeks after the initial disbursement. Such a regular repayment schedule provides several advantages in dealing with information asymmetries. First, the process serves to screen out undisciplined borrowers who recognize their inability to manage their funds to keep such a schedule of payments. The practice also helps with monitoring of the borrower, by the MFI or by the peer group borrowers, who quickly learn about cash flow issues and can address problems at an early stage.
There may be an additional benefit to the borrower of the commitment mechanism of frequent payments unrelated to asymmetric information. Poor households often have difficulty amassing funds over time due to a number of different types of diversions, including other demands on funds, requests from relations who are aware of the household’s availability of funds, and also theft (Rutherford, 2000). This limits the ability of households to collect funds over a longer time to make large payments. The frequent payments force the household to prioritize its funds and help limit the diversions that may have prevented it from amassing the savings, which otherwise would have obviated the need for borrowing.
However, the practice of frequent payments has some drawbacks. The early payments will tend to create a bias in lending to those households who have additional sources of cash income, especially when loan proceeds are used for investments that do not generate immediate cash flows. This problem is particularly apparent in agricultural lending for fertilizer and seeds for crops that will not produce cash flow until a future harvest. As Morduch (1999) points out, the MFI is effectively lending against the household’s steady diversified income stream rather than on the project itself and its particular riskiness. This will limit the usefulness of this mechanism for certain populations. (Some MFIs have loan products specifically designed for agriculture and other seasonal industries, such as tourism. The repayment schedule is adjusted to take into account the repayment capacity of the borrower, e.g., repayment may be due only at harvest.)
Microfinance loan terms may include collateral or partial collateral in a number of different forms. For example, Grameen Bank required all borrowers to contribute to an “emergency fund” in an amount proportional to the loans received (0.5% beyond a set minimum). This fund serves as insurance for group members against death, disability, and default, with payouts related to the length of membership. In addition, loan disbursements were subjected to a 5% group tax paid into a group fund account. Up to half of this fund could be accessed by members, with unanimous group consent, as a zero-interest loan. While initially not allowed, Grameen Bank now allows these funds to be withdrawn by members who are leaving the group. Although the term collateral may not be used, these funds function as partial collateral, and the group fund serves as a form of forced savings.
Some microfinance institutions explicitly require collateral—for example, Bank Rakyat Indonesia’s unit desa program. While some may point to the fact that collateral is rarely collected to show its relative unimportance, noncollection of collateral does not mean the threat of its collection did not have a big effect on the borrower’s repayment behavior and that it does not serve as an important enforcement mechanism. However, it is difficult to parse the influence of collateral from the influence of other practices of a microfinance institution, such as dynamic lending.
Lending Targeted Toward Women
One interesting practice of many microfinance institutions is to focus on lending to women. While for some MFIs, this may be connected to gender issue goals, such as increased empowerment for women, for many the simple fact that repayment rates for women tend to be higher goes far to explain this focus. However, there is substantial debate about why women are more likely to repay loans than men. Some explanations are quite simplistic, stating that women are simply more reliable and less likely to use funds for nonessential leisure purposes, including tobacco and alcohol. Looking deeper into gender differences, some assert that women are more sensitive to negative reactions of fellow members and loan officers when payment difficulties arise. Another explanation that may be more consistent with information problems is that women are simply more likely to be close to the home and thus more easily located and more easily pressured. Men may work away from the home and may more easily remove themselves from difficult situations, making it more difficult to monitor them.
Other explanations of the difference in repayments behavior hinge on societal differences. For example, in many societies, men, but not women, have alternative sources of credit, whether formal or informal. This access to additional credit beyond the MFI means men are less affected by the removal of access to credit by the MFI. For them dynamic incentives (i.e., step lending) will have less of an impact. A further societal difference is that, in some societies, women may be more involved with the type of small trade that can most effectively use and service microfinance loans.
A Potential Concern About Competition
The innovations reported above can all be viewed as alternative responses to problems of asymmetric information from the practices employed by the commercial banking industry. There is another information problem that may arise as the industry grows.
As competition in microlending becomes stronger, there is a risk that competitors may poach borrowers. The issuance of a loan from one microlender can serve as a signal to another lender that a borrower is a good credit risk. This can serve as an inexpensive screening mechanism of borrowers. Like the problems of information asymmetry, this information problem can also lead to a market failure. The poaching of clients from other MFIs may reduce the incentive for MFIs with sustainability goals to incur the initial costs of screening a new borrower, meaning competition may lead to reduced lending (Peterson & Rajan, 1995).
Empirical Findings on Alternative Practices Used in Microfinance
Microfinance has developed a number of innovative lending practices to deal with problems of asymmetric information and the particular situations of the poor. But given there are both drawbacks as well as benefits to the different innovations, which types are most effective? Within each type of innovation, which aspects of the practices are most important? And what is it that makes them work? These are some of the economic questions that arise in examining microfinance innovations.
To answer these questions, ideally, researchers would set up an experiment with a large group of households receiving microfinance using different types of contracts and compare the outcomes. This is difficult to do in the field, especially since MFIs tend to specialize in the types of lending terms they use. While poverty outcomes are certainly the primary interest, repayment rates have been the primary focus of empirical research, with many of the studies looking specifically at the effectiveness of group lending.
Some researchers have conducted experiments in a lab setting to better understand how group lending works (Abbink, Irlenbusch, & Renner, 2006). A key question is the importance of social ties on repayment rates. The experiment compared outcomes when groups were formed randomly to outcomes when groups were self-selected, presuming that the latter were largely among people with preexisting social ties. The results indicate that the groups with stronger social ties performed no better (and sometimes worse) in terms of repayment rates than groups with no social ties.
This result seems counter to the standard description of group lending that relies on social ties for selection, monitoring, and imposing social sanctions as an enforcement mechanism. However, field studies of group lending in Guatemala (Wydick, 1999) and in Thailand (Ahlin & Townsend, 2003) are consistent with the experimental results, finding that strong social ties have little or even a negative impact on repayment rates. In contrast, studies of “social capital” in Peru (Karlan, 2007) and “social cohesion” in Costa Rica (Wenner, 1995) find positive effects of their increase. It appears that the definitions of social ties used may be key to the findings. While Wydick (1999) found a negative impact of increased social ties (friendship), his measure of social cohesion, proxied specifically by living proximity or knowing each other prior to joining the group, had a positive impact. This is consistent with the earlier mentioned problem that social ties that are too close may be counterproductive.
Note that even with what seem to be good data, evaluating the effects of various microfinance practices is difficult because of selection and other problems. Armendariz de Aghion and Morduch (2005) specifically point out the difficulties of assessing group lending by discussing in more detail the Gomez and Santor (2003) study of two Canadian microlenders that make loans both individually and in groups. Because most microfinance is targeted to the poor in developing countries, to some extent, these programs differ from the norm. With much more developed financial systems, legal systems, and so on, microfinance in more developed countries operates in a very different paradigm, as can be seen by default rates much higher than usually reported. Still, both the methodology and the findings are instructive.
In the two programs studied, while interest rates and fees are similar, there are substantial differences in the populations and loan sizes between the portfolio of individual-based loans and the portfolio of group lending loans. Individual loans tend to be larger ($2,700 vs. $1,000). Group borrowers tend to be female, Hispanic, and immigrant while individual borrowers tend to be male, Canada born, and of African descent. A comparison of the two portfolios shows that group lending loans are more likely to be repaid with 20% default rates versus 40% default rates for the individual loans. However, before attributing the differences in repayment rates to the loan methodology, it is important to take into account the differences in the populations.
The approach taken by Gomez and Santor is to follow the “matching method” approach of Rosenbaum and Rubin (1983). Using a sample of almost 1,400 borrowers, the method involves first pooling all of the data and estimating the likelihood that a borrower will have a group loan (rather than a standard individual loan). Determinants include age, income, neighborhood, education level, and ethnicity. The estimates yield an index of the probability of taking a group loan, with the important feature that borrowers within the same level of the index also have similar observed characteristics. Reliable comparisons are thus achieved by comparing only borrowers with similar levels of the index. … Using this method, Gomez and Santor find that borrowers under group contracts repay more often. The result, they argue, arises both because more reliable borrowers are more likely to choose group contracts and because, once in the group contracts, the borrowers work harder. (Armendariz de Aghion & Morduch, 2005, pp. 104-105)
However, as Armendariz de Aghion and Morduch (2005) point out, this methodology only works if the variables used to develop the index are the only variables that matter in the choice of contract. If there are important omitted variables, the method will not produce consistent results. For example, suppose that borrowers who are higher risk tend to have a relative preference for individual loan contracts as opposed to group lending programs. If so, more of the high-risk borrowers would end up borrowing with individual loan contracts. If these high-risk borrowers are more likely to default, then individual loan contracts will have higher default rates. However, this would not be because of the contract design but because of the selection bias in the type of borrower who chooses the individual loan contract. In this case, if unable to identify high-risk from low-risk borrowers, an interpretation of results from an econometric model that indicates group lending increases repayment rates would be spurious.
Interestingly, further results from the experiments of Abbink et al. (2006) find that groups had higher repayment rates than would be expected if loans were to individuals. They also find that larger groups do worse.
As to the effectiveness of other practices, results are fewer. However, Abbink et al. (2006) do find that women are more reliable.
Evaluating the Impact and Effectiveness of Microfinance
The experiments and field studies just detailed shed some light on the effectiveness of group lending and other practices on MFI performance, particularly repayment rates. However, the ultimate question for microfinance is not about MFI performance but about how the lives of poor people are affected. At the most basic level, economists ask what are the full social costs and benefits of using microfinance as a development strategy targeted toward the poor. In terms of benefits, how has microfinance affected the lives of borrowers and their families? Does the use of microfinance services lead to increased income and standards of living? Perhaps just as important, does it lead to less vulnerability to negative shocks/bad events, especially those out of their control (weather, job loss, illness, family death)?
Although microfinance institutions provide many examples of borrowers who are doing quite well, these stories are only anecdotal. A more complete assessment is needed as even on the anecdotal side, there are concerns. For example, Montgomery (1996) worries that group lending puts such high pressure to repay on poor households that it may actually make them worse off in the event of a negative outcome, compelling them to pay even when difficulties arise beyond their control. Examples of how households can be harmed include stories of forced seizure of household utensils, livestock, and so forth of defaulting members.
To fully assess the benefits, one must understand the goals of the microfinance sector. Unfortunately, not all participants agree. While the issues of income or standard of living raised above are certainly an important area of concern, some MFIs view their role as more than just providers of financial services. Their goals may extend to providing health education, increasing the education of children, improving health and nutrition, empowering women, basic business training, and so on. Given the multiple goals, assessment of the success of a microfinance program involves more than an examination of repayment rates and requires a variety of data.
The number of good, academic studies of the impact of microfinance on poor people is few but growing, and some are discussed below. Importantly, the industry itself is making an effort to improve impact assessment. Hashemi, Foose, and Badawi (2007) present the efforts of the Social Performance Task Force, which met in Paris in 2005 and agreed on five Dimensions of Social Performance to guide them in designing standards for reporting to better analyze the success of microfinance in positively affecting people’s lives. The difficulty of such an assessment is apparent in the list itself. The five dimensions are as follows:
- Intent and Design. What is the mission of the institution? Does it have clear social objectives beyond providing access to credit and other financial services to poor people?
- Internal Systems and Activities. What activities will the institution undertake to achieve its mission? Are systems designed and in place to achieve those objectives?
- Output. Does the institution serve poor and very poor people? Are the products designed to meet their needs?
- Outcome. Have clients experienced social and economic improvements?
- Impacts. Can these improvements be attributed to institutional activities?
For a microfinance institution to collect data on all five dimensions is a large task. MFIs have increased their reporting capabilities on the financial side to gain greater access to both donor and commercial funding. However, this is concentrated only in the first three dimensions. The last two dimensions necessary to evaluate their social performance are much more difficult. Data collection is expensive, and an MFI may find it hard to justify expending its limited funds on this activity when the apparent need among its potential borrowers is so great. However, to fully attribute any impacts on households of borrowing and other financial and nonfinancial services, researchers must collect data on other household factors that can affect outcomes as well as from other similar households that did not use microfinance services. Surveys to collect such data require careful design and can be expensive to implement. While the industry’s efforts will surely provide more extensive data to analyze questions, it is likely that academic and donor-sponsored surveys will continue to be key to assessment of the industry’s effectiveness in combating poverty.
The Debate on Sustainability
As noted, data collection to assess financial performance has greatly improved, especially as many MFIs have sought access to private capital. Private capital requires a return on its investment, and to attract this type of funding, MFIs must show that their activities are sustainable (i.e., that borrower repayments are sufficient to be able to repay invested funds). However, there is continuing controversy about whether microfinance institutions should have as a goal to cover all their economic opportunity costs.
For example, Richard Rosenberg (2008) discusses an actual debate at the World Microfinance Forum in Geneva in 2008 between Mohamed Yunus and Michael Chu, a former investment banker and president of ACCION, one of the larger microfinance institutions, and now on the faculty at Harvard Business School:
The debaters argued about whether commercialization (let’s define it as the entry of investors whose primary motive is financial rather than social) is good for microfinance. Yunus thinks that it’s immoral to make money off the poor, and that the only kinds of investors needed in microfinance are ones who are willing to accept very limited profits for the sake of keeping as much money as possible in the pockets of the borrowers. Michael thinks that we can’t meet the worldwide demand for poor people’s financial services unless we can draw in private, profit-oriented capital, and that eventual competition can be counted on to bring interest rates and profits down to consumer-friendly levels in most markets.
In terms of social performance, to the extent that MFIs are profitable and self-sustaining, the need for a full assessment may not be necessary. Even those that rely on volunteer donations may not need a full assessment of costs and benefits if the anecdotal stories of how microfinance loans have improved the lives of borrowers are enough to satisfy potential donors. However, much of the sector continues to exist with the help of grants and government/taxpayer subsidies. Given scant resources from institutional donors and governments, it is important to determine whether the benefits outweigh the costs and whether these funds are well allocated or could produce a better outcome deployed on some other type of poverty reduction program.
The amount of well-designed studies on social performance is few, but an increasing number of good ones are providing interesting results. One important series of studies is based on a survey of 1,800 households in Bangladesh. For example, Pitt and Khandker (1998) find a number of positive impacts of microlending to households. Interestingly, the impacts differ by whether the loan is made to women or men. One important measure to assess is how consumption is affected. Pitt and Khandker find that household consumption increases by a substantial 18 taka for every 100 taka lent to women. However, the increase in household consumption when the loans are made to men is only 11 taka for every 100 taka lent. Part of the difference is due to how labor choices are affected. While lending has no effect on labor supply by women, men choose to increase their consumption of leisure (i.e., work less). Results on the schooling of children indicate that schooling increases for boys no matter the gender of the borrower. However, whether schooling for girls increases when women borrow depends on the lending program, perhaps an indicator that the nonfinancial emphasis of microfinance programs can have an important affect on their impacts. McKernan (2002) finds that nonfinancial aspects of microfinance programs do have an impact. She finds that the provision of the loan itself only accounts for about half of the measured increase in self-employment income from borrowings. She attributes the other half of the increase to improved borrower discipline, empowerment, and even shared information from the social network that arises from the social development programs accompanying the borrowing, such as vocational training and education about health and other issues. Pitt and Khandker (2002) also find that lending helps households to smooth consumption across seasons, indicating that entry into the programs may be motivated by insurance concerns.
However, the results are not unanimous. For example, Morduch (1999) reports on results using a subset of the same data limited to what he considers comparable households. He finds no effects on consumption or education, although his findings do indicate lending helps with consumption smoothing. Importantly, Pitt (1999) has mounted a strong defense of their original methodologies and results.
Fortunately, as noted before, there are substantial efforts to increase the availability of standardized data from microfinance institutions as well as surveys to better distinguish the effects of different types of contracts, nonfinancial program aspects, and so on on poor household outcomes. Future studies using this and other data will be able to provide additional evidence on the impact and effectiveness of microfinance in addressing issues of poverty. The industry is relatively young and still evolving. The impacts of microfinance may not be fully apparent during the relatively short time periods of most studies. As more data become available, the prospects for future research in this area are great.
For additional information on developments in microfinance there are a number of excellent Web sites devoted to the industry. For example, the UN created a Web site in conjunction with its Year of Microcredit (www.yearofinicrocredit .org). The Consultative Group to Assist the Poor (CGAP) maintains both a general Web site (www.cgap.org) and one that is targeted toward the microfinance community (www.microfinancegateway.org). The Microfinance Information Exchange (MIX) provides analysis and data (www.themix.org). The Grameen Foundation has created measures to facilitate microfinance institutions in working with their clients, the Progress Out of Poverty Index (www.progressoutofpoverty.org). A documentary on micro-finance produced by PBS, “Small Fortunes,” along with other material, is available at www.pbs.org/kbyu/small fortunes. In addition, two periodicals focused on microfinance are available online, the Asian Development Bank’s Finance for the Poor and the Microfinance Information Exchange’s The MicrobanMng Bulletin.
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