The Microcredit Summit's Challenge: Working Towards Institutional Financial Self-Sufficiency...

The Microcredit[1] Summit’s Challenge: Working Towards Institutional Financial Self-Sufficiency while Maintaining a Commitment to Serving the Poorest Families[2]

ABSTRACT

Institutional Financial Self-Sufficiency (IFS) is necessary for a Microfinance Institution (MFI) in order to obtain the large amount of funds required to reach and benefit truly large numbers of the poor and poorest households. There is no necessary trade-off between serving large numbers of the poorest households and the attainment of IFS by an MFI, as proven by the case studies in this paper.

Cost-effective identification of the poor and the poorest women is essential to maximising the effectiveness and efficiency of providing microfinance services to them. If the service is not exclusively for the poor and the poorest, it should be operated separately for them to minimise leakage to the non-poor.

The total cost of efficient microcredit to the poor, i.e., the appropriate interest rate, will vary between 35% to 51% of their average loans outstanding, depending on the conditions under which it is provided, and on the quality of the loan portfolio.

The poorest women in Asia, Africa and Latin America are proving that they can and will pay the required cost of this opportunity to reduce their poverty and to provide a better future for their children. This is made possible by the impressive returns to their micro-enterprises, averaging normally more then 100%.

Working toward Institutional Financial Self-Sufficiency[3]* (IFS) is essential for Microfinance Institutions (MFIs) to reach and benefit significant numbers of the poorest* households – those living in the bottom 50% of the poverty group[4] – with financial services for poverty-reduction. IFS reflects an MFI’s “ability to operate at a level of profitability that allows sustained service delivery with minimum or no dependence on donor inputs” (Christen et al, 1995, p. vi), international agencies, or charitable organizations. We believe that only by pursuing commercially-motivated, for-profit strategies will MFIs, particularly those working with the poorest, achieve our primary goal of reducing poverty among truly large numbers of the poor and poorest.

… as MF[I]s begin to wean themselves away from their dependence on subsidies and start to adopt the practices of good banking they will be forced to further innovate and lower costs. Not only may this ultimately mean better service for poor borrowers, but more importantly, it is argued that as MF[I]s become profitable they will be able to increasing[ly] tap into the vast ocean of private capital funding. If this happens the microfinance sector as a whole will soon be greatly leveraging the limited pool of donor funds and massively increasing the scale of outreach in ways that it is hoped could begin to make a truly significant dent on world poverty.[5] (Conning, 1998, p. 2)

IFS is defined as the ability of an MFI to cover all actual operating expenses, as well as adjustments for inflation and subsidies, with adjusted income generated through its financial services operations. Inflation adjustments are twofold: (i) to account for the negative impact, or “cost” of inflation, on the value of your equity* and (ii) to account for the positive impact of the re-valuation of non-financial assets* and liabilities* for the effects of inflation. Similarly, there are two types of subsidies which must be adjusted for: (i) explicit subsidies to properly account for direct donations* received by your MFI to cover operating expenses and (ii) implicit subsidies to account for loans received by your MFI at a below market rates and in-kind donations such as rent-free facilities, staff paid by third-parties, technical assistance, and the use of a third party infrastructure (e.g., communication facilities, etc.).[6] In analyzing your MFI’s performance, such adjustments are necessary as MFIs often operate in highly inflationary environments and/or receive significant “support” from third parties – such as government or donors – in the form of implicit subsidies. The adjustments take this “support” into account and allow an MFI to understand the potential commercial viability of its financial services operations. This is done by comparing adjusted operating income to adjusted operating expenses. If the figure is greater than 1.0, we say an MFI has reached IFS. If IFS has not been achieved, the withdrawal of such “support” could ultimately result in the failure of an MFI, with potentially disastrous effects for the poor clients being served.

So MFIs wanting to reach and benefit truly large numbers should be consciously working toward IFS. This does not of course mean that IFS should be attained at the cost of the overriding goal of poverty-reduction. That would defeat the purpose for which we are working – which is not profit as an end in itself, but poverty reduction. Rather it means that IFS should be pursued at a rate that is consistent with substantial poverty-reduction. Attainment of both goals must be monitored so as to ensure that IFS does not displace the more important goal of poverty-reduction.

Even with this qualification, many may disagree with the need to work towards IFS. Perhaps most would argue that NGOs have important social objectives that cannot be executed in a financially sustainable manner. Requiring that an institution do so would result in goal displacement. Outreach and service to the poor and poorest are more important, some might well argue, than making profits. It is a major purpose of this paper to try to convince those who want to reach and benefit truly large numbers, say at least 500,000, of the poorest households with microfinance, that they must aim for IFS, and that it will support, rather than displace, their efforts in poverty reduction.

The most important reason is funding. Reducing poverty significantly, that is reaching and benefiting truly large numbers of poor and poorest households, say even the 500,000 mentioned above for example, requires vast amounts of funds. Assuming an average loan outstanding per client of only US$150, for example, the total annual loan fund requirement alone would be US$75 million.[7] Add to that equity requirements to cover operating losses in the early years of operations and large-scale expansion, and the figure rises further. Attainment of the Microcredit Summit goal of reaching 100 million of the poorest households is estimated to cost around US$21 billion! From where are such vast amounts of funds going to come? Not from donors, whose funds for supporting microfinance are limited. Probably not from governments either, because of competing claims on their funds; though in countries where funds are made available by governments, MFIs should take advantage of them – provided they can do so without incurring crippling interference in their operations.

Grants and soft loans have played, and continue to play, major roles in financing MFI start-ups. They are particularly useful at that early stage when equity is usually nonexistent and deficits are large. Guarantees* and quasi-equity*, which are themselves soft loans, can also be of critical importance when the MFI seeks to establish relations with banks. However, grants and soft loans are always limited in supply and time-consuming to secure. For these reasons they are likely to be insufficient for financing the scaling-up of MFIs to reach truly large numbers and IFS.

In the likely event that grants and soft loans do not meet funding requirements for scaling-up, MFIs must search elsewhere. Only formal financial institutions are likely to be able to provide the vast financial resources required to reach large numbers of the poor and poorest with microfinance.[8]

If profit-oriented, formal, financial institutions are to be interested in entering business partnerships with MFIs, the latter will have to convince them of the strength of their operational and financial management, in other words, that the MFIs operate as commercially-minded, for-profit entities, just like the other clients of the financial institutions. In order to maximize the potential of this partnership, MFIs will have to build their equity, as it serves as a “lever” to obtain debt from formal financial institutions and savings deposits (where appropriate) from members. Currently, for MFIs, the most reliable long-term source of such equity is retained earnings*. To build retained earnings, MFIs will have to make profits from their outreach to the poor and poorest by reaching truly large numbers. Making profits, in the medium to long term, means the attainment of a sufficient degree of IFS and reasonable Adjusted Return on Assets* (AROA). There is no other way.

So it is not a question of whether or not we need to pursue IFS so as to be able to reduce extreme poverty in a big way, but rather of how best to go about it, without losing sight of our overriding concern for poverty reduction. The rest of the paper is focused on this.

A few years ago an influential book that included case studies of 12 MFIs in Asia, Africa, and Latin America argued that MFIs working with the poorest would experience a trade-off with IFS. Specifically, it concluded that, “at a given point in time [MFIs] can either go for growth and put their resources into underpinning the success of established and rapidly growing institutions, or go for poverty impact…and put their resources into poverty-focused operations with a higher risk of failure and a lower expected return”(Hulme & Mosley, V1, p.206). Lending to the poorest requires very small loans and results in a small average loan size. Achieving IFS however, it argued, would require a much larger average loan size. The Consultative Group to Assist the Poorest (CGAP) of the World Bank publicized this argument in its Focus Note No. 5. Many practitioners of microfinance with the poor and the poorest disagreed. We felt from our experience that there was no necessary medium to long-term trade-off, as even among the poorest loan clients average loan size tends to increase considerably over the years,[9] as clients prove their ability to repay and consequently have access to larger and/or multiple loans. Indeed, it is this "progressive" lending to large numbers of the poor and the poorest, coupled with efficiency and other initiatives at the program level, that makes the attainment of IFS possible while serving the poorest.

Despite the influence of Hulme and Mosley’s study, it is now recognized among many that the alleged trade-off is not inevitable (Christen, 1997; Christen and others, 1995; and Gulli, 1998, p. 28). A study of 11 successful microfinance programs in three continents found that, “Among high-performing programs (current authors’ emphasis), no clear trade-off exists between reaching the very poor and reaching large numbers of people” (Christen and others, 1995, p. viii), and concluded that their results showed that, “…full self-sufficiency can be achieved by institutions serving the very poor….” (Christen and others, 1995, p. 27). Thus it is not the clientele served that determines an MFI’s potential for IFS[10], but the degree to which its financial services program is well-designed and managed.

Learning from Successful MFIs Working with the Poorest

In choosing case study MFIs for this paper we were able to identify several in Asia, Africa and Latin America that are working with substantial numbers of the poorest households in their countries and are also on a clear path toward institutional financial self-sufficiency. Of these, we chose one from each continent for illustrative purposes,[11] as follows:

· the Center for Agriculture and Rural Development (CARD) a Grameen Bank replication/ adaptation based in the Philippines;

· Credito con Educacion Rural (CRECER) a Freedom from Hunger Credit with Education affiliate and village-banking program based in Bolivia; and

· The Foundation for International Community Assistance (FINCA Uganda), a village banking program based in Uganda.

These case study MFIs, from three different continents, operating in three distinct environments, and employing varied lending methodologies, provide empirical evidence that the trade-off is not inevitable.

Where appropriate we will also draw on the experience of CASHPOR Financial & Technical Services Private Limited (CFTS), which began disbursing loans in September 1997 as a fast-track commercial approach to providing microfinance services to the poor and poorest in India, utilizing the Grameen Bank methodology. It has the explicit goal of maximizing outreach to the poorest women while achieving IFS within five years. The purpose of referring to CFTS, although it is still very small, is to show that IFS can be achieved relatively quickly while serving the poorest clients, if it is systematically planned for and implemented from the beginning.

A brief statistical overview of the three case study MFIs is provided below; all data has been supplied directly by the case study MFIs. Given the dangers of comparing MFIs working in such different environments, we ask that readers analyze the case study MFIs on an individual basis.

Among our case study MFIs, each is committed to working with the poor and poorest households. Below we can see that all three case studies actually are serving substantial numbers of the poorest households in their areas of operation: CARD has over 20,000, CRECER more than 9,500, and FINCA Uganda approximately 13,915.

Table 2: Outreach to the Poorest Among Case Study MFIs

Of the three case studies, CARD is the only one using one of the cost-effective poverty targeting strategies outlined in the below section. It identifies its potential clients on the ground by using the CASHPOR House Index (CHI) as adapted to conditions in south Luzon and the offshore islands in the Philippines, followed by a Net Worth test for those living in houses between 4 and 6 points on their Index. However, CARD does not sub-divide its clients into "poor" and "poorest." For purposes of this paper, they took two samples of 100 new clients, one of which had joined the Landless Peoples' Association (the official name of the CARD Credit & Savings Program) before the CARD Bank was established in 1997 while the other had joined after. It was found that 53% of those who joined before CARD became a bank were living in the poorest category of houses, whereas 63% of those who entered after the bank had been established lived in the poorest houses. It would be safe to conclude, therefore, that CARD becoming a Bank has not affected adversely its outreach to the poorest, and that probably at least half of its client were in that category when they entered the program.

In 1997 an independent impact evaluation of CARD’s microfinance program for the poor was commissioned by the Grameen Trust, its main funder up until that time (Mahabub Hossain & Catalina P. Diaz, 1997). The results showed that CARD had succeeded in reaching the poorest, as “… nearly 70% of its borrowers have no access to land and have very poor housing worth less than 25,000 Peso (about US$650), and they received a share of loans proportional to their numbers” (Mahabub Hossain & Catalina P. Diaz, 1997, p.19). “The average labor productivity in enterprises financed by the loan was P107 per day, 34% higher than the market wage rate of P80 per day. The rate of return on capital was 117% compared to 46% (effective) rate of interest charged by CARD on the amount of outstanding loan. Employment, income and labor productivity increase with the number of repeat loans taken from CARD”( Mahabub Hossain & Catalina P. Diaz, 1997, p.20). So, definitely, poverty was being reduced even among many of the poorest clients.

CRECER does not have a specific poverty yardstick, nor does it currently intend to introduce one, but it operates in the poorest areas of rural Bolivia. Recently, a study by FFH evaluated the poverty level of the clients being served by CRECER. A summary of this study concluded that, “These results indicate that even within provinces with very high rates of poverty, the CRECER Credit with Education program is successfully reaching the relatively poorer households and not skewing program services to the relatively better-off in the program area." (Bresnick & MkNelly, 1999, p. 7) Comparison of CRECER participants with randomly selected non-participants showed no statistically significant difference in poverty status. The study therefore concludes that, "Given that on average 84% of the population in the 20 provinces in which CRECER is active were classified as poor, with 49% of the total population classified as 'extremely poor,' it is likely that the CRECER clients have a similar breakdown in poverty levels" (Bresnick & MkNelly, 1999, p. 5). We don’t have any impact evaluation data on CRECER, but one can assume from the small proportion (.2%) of its portfolio that is at risk that poverty is being reduced among its poorest clients. Otherwise, how could they repay so faithfully?

FINCA Uganda's outreach was evaluated in a 1999 study commissioned by the FINCA Head Office in Washington, DC. It concluded that, "67% of FINCA Uganda's new clients enter the program in 'severe poverty' – i.e., with a daily per-capita income [DPCI] of less than US$1. The average DPCI of this category was US$0.56. A further 22% of new clients were moderately poor (DPCI of US$1-2) with an average DPCI of US$1.39. Finally, 10% of clients were non-poor (DPCI >US$2+) with an average DPCI of US$3.44.”[12] Like in the case of CRECER, we don’t have any impact evaluation data on FINCA Uganda, but one can assume from the even smaller proportion (0.1%) of its portfolio which is at risk that poverty also is being reduced among its poorest clients.

From its beginning CFTS has identified potential clients as "poor" and "poorest" according to their score on the CASHPOR House Index (CHI), their ownership and operation of agricultural land and their possession of large farm animals. Poor households are those with 4 points on the CHI, owning/operating no more than two-thirds of an acre of irrigated agricultural land and possessing large farm animals worth less than rupees 8,000 (about US$190). The poorest households are those with 3 or less marks on the CHI, own and/or operate no irrigated land, have no large farm animals, and in which the wife works regularly in agricultural and/or domestic labor. Poor households tend to live in medium-sized houses with reinforced mud walls of between five and eight feet in height and having a permanent roof of used tiles. The poorest households live in small huts with mud walls of less than five feet with an impermanent roof of thatch. In retrospect, the poorest have been defined too low and have been found to make up only about 20%, instead of 50%, of the total poor. Not surprisingly these extremely poor households are currently under-represented among CFTS clients, all of whom are poor. As of the end of the first 1.5 years of work in Mirzapur, the poorest accounted for only 13% of the active savers. More time for demonstration effect to overcome their fears, and more suitable loan products for them will be required before larger proportions of these households at the bottom of the poverty group take advantage of the financial services being offered. In fiscal year 2000, CFTS decided to relax somewhat its operational definition of the poorest so that it corresponds to the bottom 50% of the poor in Mirzapur; the definition of poorest was thus changed from households with 2 or less marks on the CHI to those with 3 or less marks on the CHI. As of March 2000, with a more valid definition of the poorest, CFTS is reaching 20% of them.

At the same time as serving substantial numbers of the poorest households in their areas of operation, trend analysis shows that CARD has achieved operational self-sufficiency (OSS) for each of the last three years. FINCA Uganda achieved OSS for the first time for the year ending 31/12/99, while CRECER is on the brink of achieving this important goal, at 93.8%. All three have been steadily moving towards IFS over the last 4 years, with CARD nearly there at 95.9%. AROA is also improving, though it remains negative among all case studies as IFS has not yet been achieved.

Figure 1: Sustainability & Profitability of Case Study MFIs

While the trends over the last four years for the most part are consistently improving, CARD’s OSS from 31/12/97 to 31/12/99 is virtually flat, fluctuating from 102.7% to 100.5% to 102.2%, worrying at first sight. OSS has not improved significantly in any year as a result of rapid expansion, especially the opening of new branches, which increases operating expenses faster than interest income because of large start-up costs for salaries of new staff and capital expenditure to equip the branches, costs which are incurred well before new loan disbursements are generating much new income. The effect, therefore, is to lower OSS temporarily until the interest income in the new branches surpasses the additional expenditures that were made to generate it. Since 31/12/96, CARD’s outreach has increased by 490%, nearly doubling in each of those three years. The closing gap between OSS and IFS reflects CARD’s transition from primarily subsidized sources of financing to primarily commercial sources of financing.

After steadily improving from 30/6/96 to 31/12/98, CRECER’s OSS remained flat from 31/12/98 to 31/12/99. The steady improvements of previous years can be credited to CRECER’s more modest rates of expansion than the other two programs – growing by 118% from 30/6/96 to 31/12/98. During the calendar year of 1999, however, CRECER undertook a more aggressive expansion plan, increasing outreach from 12,892 clients to 19,351 clients. CRECER is planning to slow its growth rate in 2000, and it should be noted that for the six months from October 1999 to March 2000, CRECER achieved OSS of 99.8%.

FINCA Uganda increased its outreach to the poor by an impressive 301% from 31/12/96 to 31/12/98, and throughout that period witnessed a gradual improvement in its sustainability. However, during calendar year 31/12/99 outreach slowed significantly from 17,225 to 20,769, due to two primary factors: (i) the failure of the Co-Operative Bank of Uganda, with whom FINCA Uganda held 30% of its cash and cash equivalents and with whom 80% of its clients kept their savings[13] and (ii) the fact that FINCA Uganda did not have a Director from April 1999 to January 2000. Achievement of OSS of 105.5% at 31/12/99 was the result of an increased annual effective interest rate from 62.3% to 87.0% during the year.

So, it is clear that MFIs serving and benefiting substantial numbers of the poorest clients in their countries can be at or near operational self-sufficiency, not too far from IFS, and making progress toward both. They need not experience a trade-off between working with the poorest and institutional financial sustainability. The rest of the paper shows how this is being done.

BECOMING A COST-EFFECTIVE MFI

Most MFIs operate in environments where their only competitors are local moneylenders, who charge rates significantly above market, often between 5% and 10% per month, to their clients – including the poorest. MFIs thus have much liberty in setting interest rates before they would be out-priced by local supply. Because MFI’s effective interest rates are set not by the free market forces of supply and demand, but rather by monopolistic or oligopolistic institutions, there is a grave danger that inefficiencies and delinquencies can flourish, but remain hidden under “appropriate interest rates,” and that innovation can be stifled. While there is no doubt that the poorest should pay full cost for their financial services, they should not be asked to bear the burden of incompetent MFI management and inefficient operations.[14]

From this perspective, achieving IFS is a cost issue, rather than a pricing issue – which is particularly relevant if an MFI is interested in serving the poorest. An article by Elisabeth Rhyne summarizing the results of the 1995 Christen and others paper recognizes that, “Undoubtedly it is more challenging to serve people with very small loans or to reach remote rural clients. However, even in relatively unfavorable settings [MFIs] had developed service delivery methods so tailored to their clientele and so efficient that clients could afford to pay the full cost of the services, making the institutions financially viable” (Rhyne, 1998, p. 6).

As cost is the key to IFS, then an MFI must consistently evaluate whether or not it serves as many clients as possible with its resources at the lowest possible costs. In other words, does it operate efficiently? By definition, the concept of efficiency is simple: to maximize output from a set amount of inputs. In practice, it is much more complex, particularly since tracking sustainability and efficiency indicators is fairly new to MFIs and consequently “industry standards,” which serve as guidelines in the business world, do not yet exist.[15] How efficient a microfinance institution can become before it sacrifices the quality of its operations is not yet absolutely defined. Some guidelines are suggested later in the paper, but the only clear guiding principle is to ensure that as great a proportion of available funding as possible reaches the hands of the poorest.

The key to attaining IFS while working with substantial numbers of the poorest is, therefore, to become a cost-effective MFI. The first step in this direction is the adoption of a cost-effective poverty yardstick to identify poor and poorest households in the villages.

Cost-Effective Targeting

In order to know we are doing business with the poor and poorest, we have to identify and motivate them on the ground in a cost-effective manner. This process of identification and motivation is often referred to as “targeting the poor.” Normally, the poorest will not come forward themselves to apply for financial services, as they will not know nor believe that the services are actually for them. Even when informed, many are likely to feel that it would be too risky for them to borrow. Only patient motivation work among them and convincing demonstration effect from neighboring poor and poorest households that do participate and benefit will encourage them to take advantage of the opportunity.

While targeting the poorest is critical to our ultimate goal of poverty reduction, if a program is not able to undertake this activity in a cost-effective manner, the potential for achieving IFS might be greatly reduced or even eliminated – jeopardizing the long-term viability of a program. Hulme and Mosley themselves raise the concern that “…targeting on the poor of credit…imposes costs of research (finding out who is eligible), communication with the eligible and monitoring to prevent access by the ineligible, which may if pushed too far outweigh the benefits of poverty reduction” (Hulme & Mosley, 1996, V1, p. 36). Fortunately, proven, cost-effective strategies have been developed and refined which enable programs to identify the poorest, while also maintaining the quality measures necessary to ensure that only the poor and poorest are admitted to the program.

While the goal of this paper is not to describe nor debate the costs and benefits of targeting strategies,[16] given its acknowledged potential to reduce the ability of an MFI to achieve IFS, we believe it warrants a brief discussion. We are aware of two existing approaches to target the poor and poorest that are proven and cost-effective. These are (i) the CASHPOR House Index (CHI) and the Small Enterprise Foundation Participatory Wealth Ranking (PWR) system.[17] The CHI uses the house and compound of the household as a crude indicator to eliminate non-poor households from initial consideration as potential members, in place of the more traditional costly and time-consuming household interview. Only after the CHI identifies potentially poor and poorest households do field staff visit house-to-house to verify the eligibility of the occupant households through a short interview that focuses on the value of their productive assets.

Instead of using the house, PWR relies upon the knowledge of the villagers themselves to identify the poor and poorest among them, again eliminating the initial interview process. Villagers are called upon to map the village and to rank households into groups by poverty status, and only then do field staff interview those who have been identified as eligible. On average, both methodologies take about five minutes per poor and poorest household.

The targeting method one chooses from the above should depend on local conditions and expertise. Whichever is chosen, however, it will be cost effective because care has been put into designing both methods so as to eliminate unnecessary expenditures. Time consuming, costly interviews to determine household income or expenditure, which are of dubious validity and reliability anyway, are not utilized in the initial stages. They are replaced by a quick survey of household productive assets, which takes only about five minutes on average, and these asset interviews are done only at the final stage of targeting, after most non-poor households have been eliminated. As most of the households identified through the CHI and PWR turn out to be eligible, the interview doubles as the first step in motivating poor and poorest households to take advantage of the financial services being offered.

Exclusive Focus on the Poor and Poorest?

By focusing their efforts exclusively on the poor and the poorest, MFIs can use funds allocated for their use most effectively and efficiently. As these funds are normally limited in supply, it is vital to ensure they get into the hands of the intended beneficiaries. Leakage to the non-poor should be minimized.

There is a counter argument, however, that “It is scale, not exclusive focus, that determines whether significant outreach to the poor is achieved” (Christen and others, 1995, p. 24). Programs serving several strata of clients, not just the poor and the poorest, may be able to expand faster and to reach larger numbers. If they do, large numbers of the poor and the poorest may benefit. Moreover, such programs have the possibility of cross-subsidizing lending to the poorest from their more profitable lending to the non-poor due to larger initial average loan size, and thus could achieve IFS more rapidly.

Whether or not such “mixed” programs benefit large numbers of the poor and the poorest is an empirical question. If they do, they are surely welcome; but they should not, under any circumstances, channel funds meant for the poor and poorest into the hands of the non-poor.

Administrative Efficiency*

Most managers focus heavily on one component of institutional efficiency – administrative efficiency. This figure reveals how much it costs your institution to keep one dollar[18] of credit in the hands of your loan clients. Current “best practice” assumes well-managed MFIs should be able to achieve levels of between 15% and 25%, or administrative costs of 15 to 25 cents for every dollar outstanding, regardless of the lending methodology (Christen, 1997, p. 172). Trend analysis shows that none of the case studies has reached Christen's standard, although CARD and CRECER are approaching best practice standards.

Figure 2: Administrative Efficiency of Case Study MFIs

Over the four year period analyzed, both CRECER and CARD have showed dramatic improvements in administrative expense per average dollar outstanding; FINCA Uganda, however, has consistently reported declining efficiency. That being said, CARD and CRECER have experienced inter-period declines in efficiency – CARD in 1998 and CRECER in 1999 -- due to their higher rates of expansion. We have seen above that rapid rates of expansion, particularly the opening of new branches, result in large increases in administrative expense – primarily salaries -- before any additional loans are disbursed. Thus, administrative efficiency would fall temporarily until counteracted by an increase in loans outstanding.

Despite CARD’s consistent, dramatic increase in outreach over the four year period, expanding by 490%, overall CARD’s administrative efficiency has improved from 60.6% at 31/12/96 to 30.6% at 31/12/99. This can be credited to: (i) intensive efforts on the part of the Board of Directors, management and staff to intensify cost reduction measures at all levels; (ii) increased staff productivity resulting from an intensified incentive scheme; (iii) continuous improvement in the credit and savings methodology that resulted in better efficiency; and (iv) regular monthly evaluation of actual performance against targets at the branch level.

While showing a strong positive trend in previous years, in 1999, CRECER’s administrative efficiency declined from 33.7% at 31/12/98 to 38.5% at 31/12/99. Why? In order to allow for their rapid expansion in 1999, it was necessary for CRECER to hire a significant number of new staff, with total staff rising from 59 at 31/12/98 to 103 at 31/12/99. CRECER expects efficiency to again improve in 2000 as expansion is slowed and activities are consolidated. A factor that affects CRECER’s costs, and therefore administrative efficiency, on an ongoing basis is the provision of education services (in health and nutrition, better business and self-esteem) integrated into the microfinance services. Since these services are delivered by the same staff at the same time as the microfinance services, the additional costs of the education services are minimal. In 1999, Freedom from Hunger published a research paper in which it estimated that the “cost of education” represented approximately 6% of total operating costs from 1996 to 1998.

For the period from 31/12/96 to 31/12/98, FINCA Uganda's low administrative efficiency was credited to their rapid growth strategy, achieved through up-front investment in capacity building; remember, FINCA Uganda grew 301% during that period. However, declining administrative efficiency in 1999, from 71.8% to 76.3% on an unadjusted basis, was credited to five other factors: (i) a 30% increase in all salaries to keep them competitive with the market; (ii) an increase by 50% in fees paid to the FINCA Africa regional office for technical assistance; (iii) the provision of group accident and life insurance to clients; (iv) the provision of free stationary to clients; and (v) the shifting of some costs previously borne by the FINCA Africa regional office to the FINCA Uganda level. While the fact that some new administrative expenses have been included in the ratio that previously were not, thus impeding our ability to compare year to year results, it still remains that it costs FINCA Uganda over 76 cents to keep one dollar in the hands of the poor, signaling a very low level of efficiency.

Field Staff Efficiency*

Salary and salary-related expenses represent the significant bulk, often between 50% and 70%, of total administrative costs. Field staff, sometimes referred to as "directly productive staff", usually make up between 60% and 70% of the total staff of MFIs. Given their disproportionate representation in the overall expense mix, managers must carefully monitor and measure field staff performance and productivity. Two basic measures are consistently employed to monitor MFI field staff efficiency: (i) average number of active loan clients per field staff and (ii) average loan portfolio per field staff. These two measures work closely together to determine how much revenue individual field staff generate in relation to their costs. For example, if loan sizes are low, field staff will need to manage as many loan clients as possible, without sacrificing quality, in order to generate revenue.

With respect to the first indicator, average number of loan clients per field staff, best practice ranges for MFIs worldwide fall between 300 and 500 clients, irrespective of the lending methodology employed (e.g., individual, solidarity group, village banking) [19]. Developing a best practice range for average loan portfolio per field staff, however, is more difficult as it relies more heavily upon the lending methodology employed, the level of poverty of participating borrowers, and the local operating environment (e.g., inflation). In the Grameen Bank methodology, best practice yields a loan portfolio per field staff of more than US$25,000.

In the figures above, we see that CRECER’s loan client to field staff ratio has improved slightly from 274 to 280, nearing best practice levels; the improvement in 1999 came despite the rise in field staff from 47 to 62, which was more than offset by the significant increase in outreach to clients from 12,892 to 19,351. This coupled with a high loan portfolio per field staff of more than US$45,000, indicates high levels of field staff efficiency for CRECER. CRECER expects field staff efficiency to improve in the coming year as its revised incentive scheme takes effect.

Where CARD was moving towards best practice levels with 242 active loan clients per field staff at 31/12/98, a massive increase in field staff in 1999 – rising from 74 to 176 – drove the ratio down to 162 at 31/12/99. The rise in field staff also had a negative effect of loan portfolio per field staff, pulling it down to US$21,000. This should be a temporary phenomenon and it can be expected that both figures will rise as new field staff increase their client load.

FINCA Uganda, on the other hand, has the highest loan client to field staff ratio among our case study MFIs with 399 clients per field staff, but falls well below CRECER and is just even with CARD in terms of loan portfolio per field staff at onlyUS$21,751. Looking more closely at this, the latter reflects both the high proportion of borrowers in early loan cycles and the fact that is has the lowest average loan outstanding per loan client of US$54, compared to US$83.3 and US$163 for CARD and CRECER respectively. FINCA’s low average loan outstanding is credited to the large number of poorest borrowers in the program as well the requirement that borrowers must save 20% of their borrowings in each cycle. This latter policy is currently being reconsidered. If savings requirements are eased, the average loan outstanding should grow and thus improve the loan portfolio per field staff measure – as well as overall administrative efficiency.

New Management Tools for MFIs

Efficiency is a dynamic, not a static, process. Though there is a tendency to rely primarily on specific output measures – administrative expenses and field staff productivity – in order to understand the efficiency achievements of MFIs, alone they certainly do not tell the full story. Each of the MFIs analyzed in this paper have exhibited a willingness to tailor their services to their local environment and to embrace new operational and financial management techniques, which have dramatically increased their efficiency, and thus their ability to achieve IFS. They have paved the way for other MFIs working with the poor and poorest to follow this lead so that IFS becomes an achievable standard for all.

Access to timely, accurate, and detailed information on the overall performance of an MFI is required if efficiency and IFS are to be achieved. Management information systems (MIS) – whether manual, computerized through spreadsheet, or computerized through advanced computer-programming software – must be introduced and then updated as both financial and operational management techniques become more sophisticated. Though costs are involved in developing such systems, they are absolutely required by all to reach and serve with quality large numbers of poor and poorest households.

A cost-effective MIS should generate both financial and operational information. On the financial side, full financial statements, including the Income Statement, the Cash Flow Statement and the Balance Sheet should be prepared regularly, at least on a quarterly basis, though monthly statements would be preferable. Financial statement monitoring report formats[21] that facilitate the analysis of IFS and efficiency, among other important performance indicators, are now available to MFIs worldwide. As these new tools greatly enhance the ability of MFIs to monitor financial performance, they should be adopted.

On the operational side, in recent years portfolio-at-risk* has replaced the repayment rate as the leading measure of loan portfolio quality, following the lead of traditional commercial banks. This relatively new and valuable measure of loan portfolio quality compares the remaining outstanding balance of loans with at least one installment overdue for a specified period (e.g., one week, one month, 90 days) to the total loan portfolio. It is an indication of the proportion of loans outstanding that may not be able to be recovered in the future. It does not replace the repayment rate (amount collected over the amount due for a specified period), a historical measure, which shows what proportion of principal and interest due during a specified period actually was collected. Portfolio at Risk should be monitored on a weekly basis at the branch level and on a monthly basis for each field staff. In addition, the aging of arrears is done to calculate portfolio at different levels of risk (i.e., with arrears overdue for different lengths of time, e.g., number of days or weeks), and can be used also for calculating MFI-specific loan loss provisions.[22]

With such information, managers are able to make informed and timely decisions about performance, allowing for identification of areas where performance improvements must be made before small problems become crises. Without such information, and verification of this data through both internal and external audits, MFIs will not be in a position to make decisions that can facilitate efficiency and IFS.

CRECER implemented a computerized monitoring system (through Excel spreadsheets) back in 1995, well before it became a leading topic of microfinance “best practice.” In calendar year 1999, CRECER introduced a more sophisticated integrated MIS, introducing new reporting formats that facilitate information collection and allow for better analysis of key indicators at the zonal, regional and national office levels. This is part of a greater decentralization of the administrative and accounting functions to the regional and zonal offices. The zonal offices are now producing monthly loan portfolio and operational data reports, which are aggregated by the regional office. The regional offices themselves are responsible for preparing full financial statements. CRECER added staff at the regional and zonal levels to ensure this system worked. Results are compared against planned targets for the same period. With respect to full computerization, CRECER is currently evaluating two options for upgrading its MIS: (i) hiring a local computer-programming firm to design an integrated system, which would require 2 years to implement at a cost of US$100,000 or (ii) purchasing an off-the-shelf integrated (loan and accounting) software package, A-3 Partners, which is being developed by CAYLX, supported by Catholic Relief Services and is specifically designed for village banking programs

In the past, CARD monitored its quarterly results manually through spreadsheets. Since becoming a bank in 1997, they recognized the importance of a more sophisticated MIS. As a result, a CARD programmer internally developed the Loans Monitoring System (LMS) using a DOS based system compiled under Clipper language; the LMS has now been installed in all branches. To fully conform with the documentation and reporting requirements of the Bangko Sentral ng Pilipinas (BSP), the Rural Banker software was also adopted. This software operates within the policies and requirements of the BSP; in order to facilitate CARD’s use of the system, it was customized to their needs. To speed the flow of transactional information between the branches and head office, a main server was established. This allows for the preparation of daily trial balances and financial statements. Through this MIS, CARD is able to generate on-time and accurate reports, including financial and operational performance ratios required for management to make up-to-date decisions and, if required, policy changes. Like CRECER, actual performance is compared to planned targets.

Like the other case study MFIs, reflecting a critical theme to achieving IFS, in the past FINCA Uganda monitored both its operational and financial performance, including the full Income Statement, Balance Sheet, and Cash Flow Statement, on a monthly basis through Excel spreadsheets. Recognizing the limitations of the “manual” system, in August 1999 the loan tracking system was automated using a system called GMS. At this stage, accounting is still done manually, though there is a plan to automate this function and integrate it into the loan tracking system beginning in March 2000. Actual financial and operational performance is measured against planned targets during each period.

Monitoring results carefully and frequently is particularly important for new MFIs like CFTS, established with IFS as a primary goal. Weekly monitoring of portfolio-at-risk at the branch level enabled CFTS to determine quickly the impact of various measures taken to combat a steep rise in arrears in the second half of 1999, and to get it under control by the end of the fiscal year. Computerization of the MIS, with a customized Grameen Banker, has resulted in even more timely and accurate information on portfolio-at-risk, as well as other important operational and financial indicators. For example, under the manual system, the weekly portfolio-at-risk report was available to top management only on the following Tuesday afternoon, and it was subject to human error and manipulation. Now the report is obtainable on Friday afternoon of the week concerned, and human errors and opportunities for manipulation have been minimized. This enables field supervisors to plan and conduct corrective visits in a more timely manner. On a matter like arrears, which can spread like a prairie fire, the difference could be critical.

Working in tandem with a strong MIS system should be the business planning process. Historically, business planning – undertaken by all commercial ventures -- has not been at the forefront of MFI management. And where such efforts have been undertaken, it has often consisted of senior managers "guesstimating" important figures, such as outreach and funding requirements, in determining their goals for the following year. This is extremely unfortunate for it ignores the underlying dynamics of an MFI’s business. By undertaking the business planning process – and specifically financial modeling – managers can begin to understand how different financial and operational decisions affect various aspects of the business, and more importantly, the extent to which this impact is positive or negative.[23]

While it was once often necessary for programs to bring in specialists to develop detailed financial models, MFI-friendly tools have been developed recently and are available to MFIs[24], including the requisite management training, to develop detailed five-year financial forecasts. In doing so, management will be able to better understand the dynamics of their business and make the critical and often difficult decisions required to plan for efficiency and IFS concurrently. With planned targets developed, which map the path to IFS, managers can compare these with actual performance to determine where adjustments need to be made within the organization in order to stay on track to achieve IFS.[25] As indicated above, CARD and CRECER are already doing this.

Until 1998, neither CARD nor CRECER prepared detailed three- to five-year financial models. They did, however, develop annual operating budgets revised and updated each year based on historical performance. In the case of CRECER, this not only led to planned targets for key operational and financial data, but to the development of full financial statements. Measuring their actual performance against these well-thought-out annual budgets allowed managers at both CARD and CRECER to make changes to their operations (or, when necessary, to the financial model) to meet their primary goals. Going forward, CRECER intends to introduce a more sophisticated planning tool. In the summer of 1998, CARD introduced and is now using CGAP’s Microfin model, a sophisticated yet user-friendly financial modeling tool.

Prior to 1997, FINCA Uganda , like CARD and CRECER, prepared an annual operating plan and budget against which actual performance was measured. This included detailed operational and financial performance indicators, including full financial statements. In 1997, FINCA Uganda developed a three-year strategic plan, augmenting the annual operating plan and providing a medium-term forecast of the MFI’s goals. In 1999, like CARD, FINCA Uganda has adopted CGAP’s more sophisticated Microfin model.

The basic design and path forward for CFTS were formed from the results of detailed spreadsheet modeling. Created with the dual goals of achieving IFS and rapidly expanding outreach, an optimum program size was identified – six branches to reach 18,000 poor and poorest women in the Mirzapur District of Uttar Pradesh State, India – which would allow for achievement of both goals within five years. Thus, from the outset, CFTS had clearly defined objectives, of which all managers are aware, in regard to both financial and operational achievement. Comparing these targets to actual performance allows CFTS to consistently track where it under- and over-performs and to make the necessary changes on the road to IFS.

After 2.5 years, CFTS has added six more branches to make a total of 12 and now covers the whole of the Mirzapur district. It is still basically on track for the attainment of its goals of providing microfinance services to 36,000 poor households (with the very poor proportionally represented), and thereby attaining institutional financial self-sufficiency in 5 years. This can be seen in the figure below.

Table 3: CFTS’ Achievement of Key Targets in the Business Plan as of End Year 2.5

As plus or minus 10% of planned targets is considered good performance and plus or minus 20% satisfactory, CFTS can be said to have performed well on active savers and satisfactorily on the other three indicators. Overall, however, these results show that IFS can be systematically planned and implemented, while keeping a focus on the poorest. They also illustrate the importance of being able to evaluate actual achievement against planned targets.

Innovations in maintaining loan portfolio quality in the face of progressive lending, and thus a growing loan portfolio, as well as expanding numbers of loan clients, can be referred to as client incentives. As argued earlier, progressive lending, which provides for increasing maximum loan sizes as borrowers progress from one loan cycle to another, is critical for both poverty-reduction and the attainment of IFS, as it allows for important economies of scale. But if the quality of the growing loan portfolio is not maintained, the net result to the MFI could be negative– and in a worst case, disastrous.

In addition to the “carrot” of progressive lending, an innovative “stick” has been introduced at some MFIs. Both CARD and CFTS link the maximum loan size of subsequent lending cycles to repayment performance in the existing cycle. In other words, the subsequent loan size declines by a pre-determined amount for each dropped repayment; after a certain number of dropped repayments, four in the case of CARD and five for CFTS, the borrower is no longer eligible for a subsequent loan. CARD adds a further twist to this formula by linking subsequent loan size to attendance as well, where one absence (or two late arrivals) is equivalent to one dropped installment. CFTS delays subsequent loan disbursement by one week for every absence from or tardiness at weekly meetings. Not only does this allow CARD and CFTS to track those borrowers who may have trouble repaying subsequent loans, it also reduces the amount at risk in those loans. At the same time, credit discipline is strengthened.

CRECER has two “carrots” to induce their members to repay: (i) progressive lending and (ii) non-formal adult education in health and nutrition, family planning, better business development, and self-esteem, as well as access to contraceptives through a community based distribution system and health service referrals. Upon strong repayment performance and solid self-management, CRECER also provides members at the beginning of the fourth loan cycle with the option to increase their loan term from four to six months with biweekly rather than weekly repayment. Management is also considering offering preferred rates in the future – like any other bank – to its stronger clients, but will await achievement of IFS.

The following trend analysis shows the progressive lending among the three case studies and compares this to loan portfolio quality, as measured by portfolio-at-risk:

Figure 4: Maintaining Loan Portfolio Quality of Case Study MFIs

Of the case studies, both CRECER and CARD have shown significant increases in average loan size outstanding per loan client over the 4-year period being measured: CRECER from just over US$96 at 30/6/96 to nearly US$163 at 31/12/99 and CARD from US$59 to US$83. However, FINCA Uganda’s average loan outstanding has stayed basically flat at around US$50, though it increased slightly in 1999 to US$54. As highlighted in the discussion of field staff efficiency, FINCA Uganda has many early cycle borrowers who are taking smaller loans thus pulling the average figures down, resulting in a flat average loans size outstanding.

Critically, in the face of increasing loan size outstanding and client growth, all programs have been able to maintain strong loan portfolio quality, as measured by portfolio-at-risk. Only FINCA Uganda, at 1.9% but still well within reasonable best practice ranges, is above 1%. These MFIs, particularly FINCA Uganda and CARD, will have to focus on increasing the average loan size outstanding going forward. Without progressive lending, their progress in reducing poverty will be slow, as will be their attainment of IFS.

As highlighted in the discussion of field staff efficiency, salary and salary-related expenses represent more than half of the total administrative expenses in nearly all MFIs worldwide. In order to stay on track to achieve IFS, staff must perform as productively and efficiently as possible, while maintaining the quality of their work. Motivation of staff through incentives is a practice frequently employed in the business world. Many innovative MFIs are now experimenting with staff incentives, which link a percentage of field staff’s total compensation to predetermined performance targets tailored to the realities of microfinance, and are finding increasingly positive results.

CARD and CFTS both actively employ staff incentives in their respective MFIs. Those targeted at field staff focus on two key areas. First, incentives are linked to bringing new members into the program, particularly relevant if the MFI has just been established or is expanding. Second, in order to discourage the approval of high risk members,[26] staff are also rewarded for high loan portfolio quality, as measured by low portfolio-at-risk, and can be penalized in the event the portfolio-at-risk of the clients for whom they are responsible remains “high” for prolonged periods of time. Thus, in addition to encouraging greater labor productivity, staff incentives also reinforce the critical importance of strong portfolio quality management. CFTS, however, is moving toward full reliance on an interest income sharing incentive that requires field staff to perform all their tasks well.

In 1999, CRECER revamped its incentive system for its field staff. The incentives are three pronged: (i) loan portfolio per field staff; (ii) loan clients per field staff; and (iii) delinquency levels. As a Credit with Education program, CRECER is currently analyzing the possibility of incorporating modified incentives for field staff providing “education” services to its clients, but at present these have not been implemented.

FINCA Uganda introduced incentives for field staff in late 1996. The program is based on three pillars, differing somewhat from those described above. First, incentives are linked to the repayment performance (measured by the repayment rate of amount collected divided by the amount due) of a field staff’s clients during any given month. Second, FINCA Uganda compensates field staff according to the gross loan portfolio managed (adjusted for arrears). Finally, to encourage field staff to turn the loan portfolio over quickly, improving annual yields, there is a “Week 17 Recapitalization” incentive. With a loan term of 16 weeks, this incentive provides a set of cash benefits to those field staff who are able to receive client loan applications in Week 16 and then disburse the new loan in Week 17 – meaning that funds do not linger unproductively in low interest bearing bank accounts. Unlike the other case study MFIs, FINCA Uganda sets an eligibility requirement for field staff to participate in the incentive scheme. Specifically, field staff must have an average of 30 clients per village banking group before qualifying. While this incentive scheme has served them well, FINCA Uganda is seeking to simplify the process. The new scheme, which is still under consideration, will be based entirely on the loan portfolio (adjusted for arrears) managed by the field staff, and is thus indirectly based on the assumption of a certain yield level from the portfolio and the income required for the institution to meet its IFS goals. Where field staff can surpass the portfolio requirements, the incentive payment will be a portion of the additional income earned by FINCA.

CFTS has innovated further on the incentive structure for field staff. It provides differing rewards for bringing the poorest versus the poor households into the program. In fact, the compensation is 66% more for recruiting the poorest.

CARD has been a leader among CASHPOR Grameen Bank replications in providing incentives for its field staff to increase their productivity. For example, it pioneered "fast-track" promotion for new field staff. Those on the first three-month basic training can be promoted to probationary status after two months if they have recruited and trained at least 20 "quality members" (i.e., clients who have passed the Group Recognition Test). The target for probationary field staff is 60 “quality members” in six months, but they are confirmed as CARD staff as soon as they get it. CFTS has utilized the fast-track confirmation in Mirzapur India with good results in terms of group formation and staff satisfaction.

Know What Clients Want: Customizing Financial Products

Underlying the strategy for achievement of IFS is the implicit assumption that an MFI can attract new clients, and maintain the commitment and participation of existing clients. Financial products being offered, including both loans and savings products, must be designed especially to meet the needs of the poorest women. The first loan must be small enough to be easily repayable in frequent, small installments, but not so small as to be insufficient for generating additional income quickly. Study of local income-generating activities of the poor and poorest women will reveal a suitable band and term for first loans. Typically in Asia, the principal amount will range from US$25 to US$75 and the term from six months to one year. Subsequent loan and savings products of the MFI must be designed to facilitate a rise out of poverty.[27] Subsequent loan size must increase with the demand from clients and their (increasing) ability to repay. Not all clients will progress at the same speed. There should be different loan and savings products for clients of differing abilities and with different demands. The overall result, however, will be an increase in average loans outstanding and average savings balances as time passes. This offers the prospect of increased profits and income to the poor and poorest women and a steady growth in depth of outreach to the poorest. It also provides the conditions for the attainment of financial self-sufficiency by the MFIs serving the poorest and for attractive profits to the banks providing bulk loans to them.[28]

These general guidelines, however, sometimes do not fit closely with the reality of the operating environment. MFIs committed to working with the poorest must be flexible in designing their credit and savings products.

CARD discovered very early in its adaptation that a one-year first loan term, like that of the Grameen Bank of Bangladesh, was not suitable for most of its clients who were involved in tertiary activities, like petty trading, which has shorter business cycles. It reduced the term of its first loan to six months. In recent years, it has also introduced new loan products, such as the Multipurpose Loan Product, available to clients for any purpose after six months of membership in an amount up to 5,000 pesos (approximately US$132). This product is intended to discourage clients from turning to the traditional moneylender, as they had been doing, in times of trouble. It has become popular. CARD has also recently introduced the CARD Loan Accelerated Program (CLAP) for those very successful members who have been with the program for many years. Qualifying members are given an identification number that allows them to draw on an overdraft account based on the needs of their business. Such members are still required to attend weekly center meetings.

CRECER, unlike the other case study MFIs, operates in a highly competitive environment. While CRECER has avoided some of this competition by targeting the poor and poorest rural and semi-rural households (often not the primary target group for the majority of MFIs operating in Bolivia) they still must operate efficiently, maintain competitive interest rates, and provide services that their clients consider attractive compared to those of other MFIs. This is where Credit with Education comes in. It integrates the weekly credit delivery service at the village level with health, nutrition, family planning, and better business education services. While this adds an estimated 6% to the cost of the program, it is clear from the efficiency and sustainability measures provided above that the services can be delivered competitively. Recent innovations include community-based distribution of contraceptives, including condoms and vaginal tablets, as a for-profit venture. CRECER also works closely with NGOs’ and the government’s health representatives to provide discounted health services – including referrals – to its clients.

Since commencing operations seven years ago, FINCA Uganda has not introduced any new loan products, and this is recognized as a limitation of the program; plain vanilla credit will not keep FINCA Uganda competitive going forward. As a result, focus groups have been held recently with borrowers to receive their input on potential new loan products. Currently, the head office is also carrying out a marketing study. While new loan products have not been introduced, FINCA Uganda has added peripheral products for its clients including credit, life, and disability insurance. The new management at FINCA Uganda is very aware of this issue.

At CFTS, loan products were re-designed after six months of fieldwork as the one-year income-generating loan of Rs.2000 was found to be too rigid for client requirements. Recognizing this, a workshop was held with the Center Chiefs, the elected leader of each village-based center, after which the now-popular, shorter-term, smaller petty trading loans were introduced. At the same workshop, management was told that some clients on one-year term loans needed more capital during the year. As a result, two additional types of loans were hammered out and introduced: (i) the Additional Balance-Based Loan (ABBL) allowing clients who did not borrow the maximum for their loan cycle (but have a perfect repayment record) to borrow the balance and (ii) the Additional Savings-Based Loan (ABSL) allowing those who did borrow the maximum (and who have perfect repayment records) to take an additional loan after six months in an amount up to fifty times their running average weekly voluntary savings. The response was positive. In the fiscal year ending 31/3/2000, a 6-month first loan with a maximum of Rs. 4,000 was introduced as a result of client demand.

What has just been written on the attainment of administrative and field staff efficiency targets is not intended to give the false impression that it is easy. As of December 31, 1999, none of the three case study MFIs had attained all of the target efficiency levels. CARD’s administrative efficiency of 30.6% is nearing the upper level of the target range of 15% to 25% and appears on track to attain it and CRECER expects to show strong improvements in the coming year, following its significant expansion in 1999; but FINCA Uganda (at 76.3% at 31/12/99 with declining efficiency over the last four years) is not near nor obviously on track to achieve the target.

With respect to field staff efficiency, FINCA Uganda had exceeded the minimum best practice target of 300 loan clients per field staff target with 399, while CRECER was very close with 280; but CARD at 162 loan clients per field staff, declining significantly from 31/12/98’s figure of 242 given the addition of 102 field staff during 1999, is not close to the minimum target. The minimum average loan outstanding per field staff of US$25,000 has been surpassed only by CRECER, at US$45,571. Both CARD at US$20,976 and FINCA Uganda at US$21,751 remain below the target. CARD’s lower averages are due to significant increase in staff to allow for the large numbers of new clients in 1999. As discussed above, FINCA Uganda is currently reconsidering a savings policy, which restricts loan size; a change in the policy should improve this efficiency measure.

With respect to portfolio–at-risk, which can be seen as a measure of the efficiency of loan recovery, all three case studies are performing extremely well; FINCA Uganda reports the highest level of portfolio-at-risk of 1.9%, which is well within the acceptable band of 0% to 10%.

There is a lesson to be learned here. Even though our case study MFIs illustrate that there is no necessary trade-off between attainment of IFS and serving large numbers of poor and poorest households, they also show that there is still much room for improving efficiency – even among top-performing MFIs. CARD, CRECER and FINCA Uganda will have to work harder to raise their efficiency to levels required for IFS, particularly as the threat of competition looms over MFIs, which would drive interest rates down. The good news is that tools and training necessary for improving efficiency levels, as outlined above, are available today to all practitioners. These must be taken advantage of as we seek to achieve our primary goal of poverty reduction.

Administrative efficiency tends to decline initially as expansion of outreach takes place, if the latter involves the opening of new branches and/or the hiring of trainee staff. The main reasons for this are: (i) the up-front costs of opening new branches; (ii) the fact that new field staff have to be trained (and thus paid), often for many months, before they become productive; and (iii) that following their training, it takes time for their productivity in bringing clients into the MFI to reach levels of experienced field staff. In our experience, it often takes more than 5 years for new field staff to meet the target of 300 clients and $25,000 in loan outstanding. It is only after 3 to 4 years that he/she should be earning enough interest income from the loan portfolio they manage to cover their salary.

There is no running away from the fact that expansion of outreach to the poor will require the opening of new branches and the hiring and training of new field staff. Both administrative and field staff efficiency levels will drop as soon as the new field staff are included in your ongoing performance evaluation. And it will take considerable time for the economies of scale that eventually come about from expansion of outreach, to be enjoyed. So rapidly expanding MFIs will not be able to meet the levels of efficiency which allows for achievement of IFS in a reasonable time-frame and thus, for funding from commercial banks or the taking of savings from the general public. Rapidly expanding MFIs will show increasing losses, until the expansion levels-off and it begins to enjoy economies of scale.

There is definitely a trade-off between expansion of outreach and IFS, as long as the expansion involves the opening of new branches and the hiring and training of new field staff. The decline in financial sustainability that accompanies rapid expansion of outreach makes the ongoing financing of that expansion difficult. This can be called the “paradox of poverty-reduction through microfinance”: expansion of outreach is necessary for more poverty-reduction, but the expansion itself lowers IFS, which, in turn, makes commercial financing of the expansion more difficult, if not impossible[30].

Strategic planning and financial modelling can help to break the paradox. The maximum period social investors and donors will wait for IFS is thought to be about 5 years. So, based on realistic assumptions concerning client build-up, loan products, average loan sizes by cycle, repayment rates, repeat loan rates, savings products and savings mobilisation, staff salaries and allowances, other administrative costs, the cost of funds, likely leverage ratios, and the cost of additional capital, we can use financial modelling to calculate the number of clients that can be served and the appropriate interest rate to be charged to allow for IFS and the covering of all accumulated losses within 5 years. If the poor can pay the required interest rate, then we can do business with them. In the case of CFTS, modelling told us the appropriate interest rate would be 20% (flat), and we are doing business with the poorest at that rate.

To attract sufficient funding to implement the 5-Year Business Plan, it should be promoted as a “package financing” to IFS. If social investors and donors can provide grants and soft loans, contingent upon the attainment of planned annual performance targets, to finance the operating deficits prior to IFS, then commercial banks should be willing to provide the required onlending funds at commercial rates. Thus the paradox can be broken.

Can the Poorest Afford Microcredit?

Covering Your Costs – Setting Appropriate Interest Rates

As defined previously, an “appropriate” interest rate is one that will allow an MFI to cover all of its adjusted operating costs from its adjusted operating income within a reasonable period of time. Four to five years is thought to be the maximum time available as the patience of donors/investors providing grants and other “subsidized” funding is not likely to extend beyond that. As a cost plus measure (designed to cover costs plus provide a reasonable profit) in its most fundamental state, an appropriate interest rate will be determined primarily by how efficiently the organization is able to operate its business. But that is not to discount the critical importance of the final component in calculating appropriate interest rates – profit, as measured by the capitalization rate. Without profit, continuation of IFS will be impossible. Earning a profit allows MFIs to build their equity – a source of funding – in order to expand their outreach to the poor and poorest. And as the equity position grows, the MFI will then be able to further leverage funds by raising debt or taking deposits from clients (or the general public if the MFI is regulated) and increase outreach even further, driving the program towards IFS and beyond.

Setting appropriate interest rates is therefore a matter of estimating your unit costs of administration, loan loss, funds, and capital. A good set of guidelines is found in CGAP Occasional Paper No. 1. There, it is noted that “the annualized effective interest rate (R) charged on loans will be a function of five elements, each expressed as a percentage of average outstanding loan portfolio: administrative expenses (AE), loan losses (LL), the cost of funds (CF), the desired capitalization rate (K), and investment income (II)” (Occasional Paper 1, p. 1):

Based on the above formula, this means that appropriate interest rates for MFIs working with the poor in Asia range between 35% and 51% per annum.

A note of caution should be added here. For all of the reasons outlined above, achieving IFS is of critical importance to MFIs if they seek to expand outreach to large numbers of poor households. However, as we are working with the poor and poorest, a balance must be struck when setting an appropriate interest rate. It is between early achievement of IFS, and the institutional benefits this brings, and keeping the interest rate to be charged to clients manageable for them. This means that the interest rate must not be so high as to rule out adequate profitability on the main income-generating activities open to the poor; that is to say, the pace of planned achievement of IFS for an MFI must be consistent with the attainment of the overriding goal of poverty-reduction. Most importantly, an impossible burden must not be placed on the shoulders of the early clients.^[31]

Setting an appropriate interest rate is a key step in getting on the path towards IFS, but ensuring that the loan portfolio and assets yield the expected rate of return is another challenge.[32] Up to this point in the paper we have focused on managing expenses as a means to increase efficiency. However there are three tools available to measure our efficiency in managing income. In other words, is an MFI generating the expected level of income from the loan portfolio, as measured by the appropriate interest rate? If this is the case, it can be assured of overall strong management. If not, regardless of how well it manages its costs, it will be very difficult to achieve IFS.

The best way to measure income efficiency is to compare annual effective interest rate, also known as the Annual Percentage Rate (APR) – the total cost the borrower must pay for credit services in a year – with the actual portfolio yield. The portfolio yield attempts to measure how well an MFI is collecting from its clients by comparing interest and fees received from loan clients during a specific period of time (up to one year) to the average loan portfolio for the same period. Differences between the APR and the portfolio yield can imply poor loan portfolio quality or difficulty in collecting interest. Particularly for MFIs with loan cycles of less than one year, they can also imply slow administrative “role-over” of the lending product(s) due to increasing dormancy among clients on subsequent loans, among other factors, or inefficient management techniques leading to delays in subsequent loan disbursements. Taking the gap analysis one step further, an MFI should also compare interest and fee income received from loan clients during a specific period of time (up to one year) to the average total assets for the same period – referred to as the asset yield. The difference between the portfolio yield and the asset yield indicates how well an MFI has invested its other funds, those not out in the hands of the borrowers via loans, in income producing activities. For example, are other funds being kept in non-interest bearing bank accounts, or have they been placed in interest-bearing investments such as cash deposits (CDs) or savings accounts. If there is a large difference between the two, it can indicate that assets other than loans are not being managed properly.

The best-managed MFIs will show very little difference between each of these measurement tools. Institutions with moderate to large differences will note that administrative changes may be in order – of which they may already be aware based on their cost management techniques. Tracking efficiency in managing both expenses and income allows an MFI to ensure it does not stray from the path towards IFS.

The annualized effective interest rates, portfolio yields, and asset yields of our case study MFIs are as follows:

Figure 5: Gap Analysis of Case Study MFIs

Here we see that of the three, CRECER has the smallest gap between the APR and the loan portfolio yield, with a spread of only .9% at 31/12/99. This is a significant improvement from a gap of 6.6% at 31/12/98 and reflects CRECER’s increased management focus in 1999 on immediate turnover of 16 week loans. FINCA Uganda’s dramatic reduction in the gap from 12.7% at 31/12/96 to 3.6% at 31/12/99 reflects its specific efforts to improve turnover of its loan portfolio by introducing the “17 Week Recapitalisation” incentive to field staff, described in the Staff Incentive section above. However, the difference between FINCA Uganda’s loan portfolio and asset yields was the largest among our case study MFIs, a spread of 34.1%, reflecting high liquidity at FINCA Uganda. As of December 31, 1999, just over 33% of FINCA Uganda’s total assets were held in cash and cash equivalents; as FINCA Uganda gets this money out in the hands of the poor, the gap should shrink. CARD’s yield gap also closed, but less significantly, from 5.6% at 31/12/98 to 3.8% at 31/12/99.

Can the Poorest Pay Appropriate Interest Rates?

This is a sensitive issue! Politicians are fond of “defending” the poor and poorest by insisting that interest rates charged to them on loans should be subsidized and low. It is now known that subsidized credit rarely gets into the hands of those for whom it was announced, yet politicians persist. In several countries governments still cap interest rates on small loans in the mistaken belief that it helps the poor and poorest. In fact, it has the opposite and unintended result of depriving them of access to credit at all, as the scarce, subsidized credit is taken by those at the local level with more influence and better connections than the poor and poorest.

NGOs, not to mention companies, who offer sustainable financial services directly to the poor and poorest at appropriate interest rates are ironically in constant danger of being accused of usury by politicians, bureaucrats, and intellectuals – but notably not by their clients. It is necessary to defuse the issue.

As it is known, in microfinance, loans are small. Even relatively “high” interest rates on them still result in relatively small (in amount payable) installments, especially if these are paid frequently, say weekly. For example, a Peso 2,000 (about US 46$) loan for hog fattening at CARD payable over 6 months will require weekly installments of Peso 88 (about US$2.32) of which Peso 8 (about US$0.21) will be for interest. Two piglets will be purchased at around Peso 1000 each. After about six months, the fattened (mainly on household scraps, vegetables planted in the house garden for that purpose, and commercial feed supplement) pigs can be sold for about 4,000 Peso each, giving an attractive lump-sum return and net profit estimated at around 100% on average. Weekly repayment is made from the household cash flow, which sometimes requires its members to tighten their belts. Here is a classic form of savings based on self-denial for future gain.

For households too poor to tighten their belts, loan activities like petty trading or small shop-keeping, that result in the quick and frequent generation of additional income, are more appropriate. To minimize the repayment burden such loans can be for small amounts. For example, petty trading of bangles and cosmetics by poor women at CFTS India, requires a working capital of only about 1,000 Indian rupees (about US$24). If this is borrowed at an interest rate of 20% (flat) for a term of 20 weeks, with principal and interest repaid in equal amounts weekly, then the required weekly repayment is 60 rupees (about US$1.42) of which 50 rupees (US$1.18) is principal and 10 rupees (US$0.24) interest. Usually the women sell house-to-house and village-to-village, carrying their wares in a basket on their heads, six days a week, grossing about 100 rupees a day or 600 per week of which about 120 rupees (about US$2.83) are net profit, half of which goes for repayment.

For larger loan amounts, the weekly repayments can be kept small and manageable by lengthening the loan term. In India a popular loan activity among the poor is the purchase of a moderately yielding, say 3 kilo per day, milch buffalo, which can be purchased pregnant for around 6,000 rupees (about US$150). If a loan of the whole amount is made available for that purpose to a very poor women at 20% interest (flat), or an effective rate of around 40%, on a declining annual rest for a term of two years with 100 equal weekly installments of principal and interest, each payment would amount to [(6000+(6000x0.2))+(3000+(3000x0.2)= 7,800/100 =78 rupees (just under US$2)]. The 3 kilos of milk could be sold daily for approximately 12 rupees per kilo. This means that the weekly repayment money of 78 rupees could be earned in two to three days, leaving the income from the other four to five days to reduce the poverty of the household. The risk of the buffalo dying can be covered by livestock insurance at a premium of 4 rupees per week, or 100 rupees per year. Over the two-year period, the total cost would be 160 rupees, which could be paid from the sale of the milk. However, as the buffalo will produce milk for only about nine out of twelve months, the clients have to save or engage in some other income-generating effort for the remaining three months. To fill the gap, clients in India purchase a second buffalo as soon as they can. With two milch buffalo they can have a good, steady income throughout the year, with which they can pull themselves and their families right out of poverty within a few years. A good example of this can be seen at the Society for Helping and Awakening of Rural Poor Through Education (SHARE) Branch in Dachepalli, Gunter District, Andhra Pradesh, where more than half of the loans disbursed over the past five years have been for milk buffalo, and many of the original clients are now living in large concrete houses of their own design.

The examples above hint at a second important factor that makes it possible for the poor and the poorest to pay appropriate interest rates. The returns to capital in their microenterprises tend to average more than 100%. This was the finding of a recent, careful impact-evaluation study of CARD by Mahabub Hossain.[33] Returns to capital in his random sample of clients averaged 117%. As CARD’s effective interest rate on loans to clients is approximately 39% per annum, this leaves on average 78% in the hands of clients to reduce their poverty. It can, of course, be argued that if CARD’s interest rate were significantly lower, its clients could come out of poverty faster. But, from where would they get their loans? If CARD does not charge an appropriate interest rate it may not be able, in the short-term and long-term, to earn a profit, thus making it dependent on donor and government largesse. In a worst case scenario, it might no longer be able to meet the financial needs of its clients. There is no certainty that another MFI would fill the gap. The only alternative for the moderately poor and poorest may then be the traditional moneylender. A recent study of the returns to capital in microenterprises in India and Kenya (Harper, 1998), found them to be even higher on average than did Hossain and Diaz at CARD.

The consistent, near perfect repayment rates, which are characteristic of MFIs around the world, are empirical evidence that the moderately poor and poorest can pay appropriate interest rates charged by efficient microfinance institutions. Working in an area of India where repayment of Integrated Rural Development Program (IRDP) loans is said to have been less than 10%, CFTS has been able to collect 97% of weekly repayments due, since it began operations 18 months ago, and SHARE in Andra Pradesh, India has been recording perfect repayment performance since it started. CARD has maintained near perfect repayment for years, with about half of it clients coming from the poorest category. CRECER and FINCA Uganda have had the same experience while dealing with substantial numbers of the poorest. It is our impression that if anything the poorest clients have a higher repayment rate than the poor clients. Probable reasons are the strength of the desire of the poorest women to rise out of poverty and provide a better life for their children, as well as their relative lack of alternatives for earning cash income.

The 16 CASHPOR-member MFIs, who together had US$42.7 million in loans outstanding to over 315,000 poor and poorest households throughout Asia at the end of 1999, had a combined portfolio-at-risk of only 2.04%. The millions of weekly payments made in full and on time, that lie behind that figure, are eloquent evidence of the ability of the poor and the poorest to pay appropriate interest rates for their financial services.[34]

So it is clear that the poor and the poorest can pay much higher effective interest rates on loans for income generation than has been presumed by many.

What about Savings?

Very little has been said in this paper about the hoary topic of savings. Few other issues in microfinance have sparked so much debate or aroused such emotions. Our relative silence on savings is not meant to deny the importance of promoting the practice among the poor, upon which most microfinance practitioners agree.

Savings are needed to provide a safety net for the poor so that in times of emergency (e.g., food shortage or illness) they have some funds to meet their needs and to smooth deficit months so that they do not have to turn to exploitative moneylenders. Appropriate savings facilities are of particular importance to poor women who need a secure place to keep surplus funds, but one to which they themselves have easy access. Weekly meetings of an MFI with its clients in their villages provide the opportunity to supply such savings services. Through them, clients can also build up their savings so as to be able to purchase the productive assets that can make their progress out of poverty sustainable, or to perform important social obligations. There is no doubt about the importance of savings to the poor.

Most MFIs also recognize the importance of savings for the institution and its business. Client savings, if partially blocked, can reduce the risk of lending to the poor. More important, savings can be a relatively cheap source of funds for MFIs, as interest rates that have to be paid to attract savings are usually less than those that have to be paid to borrow funds commercially.

Major differences exist among MFIs, however, on the relative emphasis to be given to savings as compared to credit as tools for poverty-reduction, and on the timing of the emphasis. These differences are illustrated by the three case studies in this paper. CARD, being a Grameen Bank replication/adaptation, gives primary emphasis to credit and only secondary attention to savings. Only small amounts of compulsory savings are required for the Group Fund for a few weeks to make clients eligible for much larger loans to finance income generation, the amount of which is not related to the amount saved. As its clients progress, however, CARD increases the amount of compulsory group savings and promotes individual, voluntary savings. FINCA Uganda and CRECER, utilizing the village banking approach on the other hand, put primary emphasis on savings first. Only after saving regularly for several months does a poor household become eligible for a loan and the maximum that can be borrowed is restricted, at least in the case of FINCA Uganda, by the amount saved (five times).

The three case studies differ, as expected, in their average client savings balance, with CRECER having the lowest at US$29[35], followed by CARD at US$43 and FINCA Uganda at US$65. Notice that the ranking of the MFIs in terms of average loan outstanding is the reverse, with CRECER having the highest at US$163, followed by CARD at US$83.3 and FINCA Uganda at only US$54. FINCA Uganda’s strict linking of loan amounts to savings may have limited the amounts that could be borrowed by the poor. It follows that we find CRECER has the lowest savings to outstanding loans ratio at 20%, compared to CARD at 47% and FINCA Uganda at 119%.

In addition to these differences among MFIs in terms of relative emphasis on credit and savings as instruments for poverty-reduction and their timing, there are major moral and legal issues involved with savings. The moral issue has to do with the need for adequate protection of the savings of the poor, and the legal issue revolves around the responsibility that governments usually take, through their central banks or other regulatory agencies, for providing this protection. As a result, NGO-based MFIs usually can’t legally mobilize deposits, even from their clients, not to mention from the public. However, many governments and regulators “close their eyes” to NGO-MFIs mobilizing deposits from their members, as they realize that most are sincerely trying to help the poor. However, the question of whether there is adequate protection for the deposits of the poor remains. And the possibility of legal action against deposit-taking NGOs is always there. Ultimately, such protection can come only from the capital adequacy of an MFI, but NGOs do not usually have any equity. For this reason the amount of savings MFIs can mobilize will, and should be, restricted. So, if NGO-MFIs want to offer progressive lending to large numbers of poor households, savings cannot be expected to be the major source of funds, nor of institutional financial self-sufficiency, for these MFIs. It could still be an important source of funds, however, and should not be neglected. Once an MFI becomes financially self-sufficient and builds up its equity through retained earnings, mobilizing deposits from the public could become its major source of funds for further poverty-reduction.

Conclusion and Recommendations

The Challenge Is Being Met but Still on a Small Scale

Large numbers of the poorest households in Asia and growing numbers in Africa and Latin America are already being provided with financial services in a sustainable way. A close look at the case study MFIs has shown not only that this is being done, but also how to go about it. The key is increasing cost-effectiveness so that the appropriate interest rate to the poor and the poorest can be minimized. Comparison with CFTS has revealed how this can be systematically planned and implemented to reduce significantly the period of time required to attain IFS, by maximizing the scope and depth of outreach to the poor and the poorest – provided adequate funding is available to financial break-even for the MFI.

The key steps to increasing institutional efficiency are: 1) cost-effective targeting; 2) maximizing both institutional and field staff efficiency through management information systems, formal business planning, maintenance of loan portfolio quality with client incentives, and staff incentives; and 3) customizing financial products.

Once an MFI has planned for high levels of efficiency and managed its costs in such a way as to achieve those goals, the next step is for it to charge an appropriate interest rate and ensure the rate is yielded by its portfolio.

For mainstreaming to occur in some of the poorest countries, key policy changes will have to take place. First, interest rate caps on loans to the poor and poorest must be removed where they still exist. Second, a suitable legal identity for providing microfinance to the poor and poorest (perhaps exclusively, to minimize leakage to the non-poor) has to be created and provided with a regulatory system supportive to the overriding objective of reducing poverty through the provision of microfinance to the poor and poorest.

Attitudes also must change. Persistent concerns remain within the microfinance community, and perhaps elsewhere, that IFS is not an achievable goal for many MFIs – particularly those which began life as NGOs. Our case study MFIs provide empirical evidence that this is not true. Many NGO managers came into microfinance because of the promise it had shown, through the success of the Grameen Bank of Bangladesh and other similar microcredit innovators, for large-scale reduction of poverty. Many are gradually transforming their multi-program NGOs into de facto financial institutions because microfinance has become their most effective program for poverty-reduction. They are re-training to become competent managers of their microfinance institutions. Their motivation, and job satisfaction, do not come from banking per se but from seeing with their own eyes the increasing numbers of poor women lifting their families out of poverty and providing better lives for their children through the opportunities provided by the microfinance that their NGOs have delivered. It is hard to imagine a higher level of job satisfaction than is obtainable through providing microfinance to the poor and poorest in an efficient and financially sustainable way.

It is hoped that this paper has removed much of the mystique that has surrounded OSS, IFS, and AROA, and shown them to be attainable by MFIs managed by normal human beings who are motivated to reduce poverty through the provision of microfinance to the poor and the poorest, and who will take the trouble (not much, really) to learn how to do it in an efficient and financially-sustainable way.

MFIs, provided with the suitable legal identity mentioned above, should begin establishing track records with commercial banks in their countries as soon as possible. Guarantee funds* and quasi-equity* in the form of subordinated soft loans provided by responsive donors or government agencies could help attract the banks initially. Once the relationship is established, however, it will be the reliability of the loan recovery and the soundness of the financial management of the MFIs that determine the degree to which they are allowed to leverage their equity to obtain the huge amount of funds required to reach and benefit truly large numbers of the poorest households.

Five-year business plans and effective monitoring of them, which show an MFI is on track to achieve planned targets, can attract the interest of banks even before an MFI begins to make profits. CFTS has had a credit line sanctioned from a commercial bank, the Oriental Bank of Commerce, at a market rate of 14.8% p.a., and has received financing from an apex bank, the Small Industries Development Bank of India (SIDBI), at a near market rate of 11% p.a., because CFTS has an attractive business plan and is able to show that it is meeting its targets. Both banks are financing CFTS as a financial intermediary with the poor. Each has been given a 10% margin of the sanctioned amount in the form of a fixed deposit, and both have accepted loans with the poor as security. A rating by an independent microfinance institution rating agency which gave CFTS alpha minus status, meaning “recommended because of reasonable security and good systems,” was helpful in securing the line of credit from SIDBI.

Franchising to the Poor and the Poorest

To maximize their benefits to the poor and the poorest, MFIs could be sold[36] (franchised) to their clients once they attain IFS. In this way, the poor and the poorest would be able to enjoy some of the profits of providing them with financial services. Also, this should remove any remaining concern about charging them “high” interest rates. Finally, it would free the franchiser to get on with establishing more franchises to reach and benefit more of the poor and poorest households, although it would have to ensure that quality was maintained in the franchises according to the franchise agreement. In this way, truly large numbers could be reached and assisted out of poverty.

David S. Gibbons and

ABBREVIATIONS

GLOSSARY OF TERMS

Adjusted Return on Assets: A measure of MFI profitability. Specifically, how well assets – in which MFI’s

(AROA) have invested – have been managed to generate profit. The ratio, based on adjusted results, is calculated as follows:

Administrative Efficiency: Measures the amount it costs an MFI per dollar to administer its credit and savings services. The ratio is calculated as follows:

(APR): The amount an MFI charges is clients to borrow from the MFI, taking into consideration all fees, various methods of calculating interest rates (e.g., flat, on the declining balance), and the term of loans. It is the interest rate an MFI expects to realize on its loan portfolio. It is also referred to as the annualized effective interest rate.

Assets: One of three primary accounts on the Balance Sheet. Represents investments or what the MFI has purchased with its available funds. As a provider of financial services, an MFI’s primary asset is the Loan Portfolio.

Asset Yield: Measures the amount of income actually yielded from an MFI’s total assets. The difference between the Portfolio Yield and the Asset Yield indicates how well an MFI has invested all its resources, not only those in the loan portfolio. The ratio is calculated as follows:

Average Loan Portfolio: For a given period, the average balance of loans outstanding. A simplified calculation takes the opening balance of the loan portfolio on the first day of the period being measured, adds the closing balance of the loan portfolio on the last day of the period being measured, and divides the sum by 2. Measuring a precise average loan portfolio can be more complex. For purposes of this paper, the simplified methodology has been used.

Depreciation Provisioning: The cost an MFI incurs recorded on the Income Statement as an expense, representing the deterioration in value of fixed assets as they age.

Donations: Also referred to as grants. Donations are money provided by donors and other socially concerned organizations most often, though not always, to cover operating losses at an MFI. These are recorded on the Balance Sheet as equity.

Equity: One of three primary accounts on the Balance Sheet. Often referred to as the value or net worth of an organization.

Guarantee: Often provided by donors and funding agencies in lieu of direct funding (e.g., soft loans or grants, etc.) to assist MFIs in accessing new sources of funding – particularly from the commercial markets. The idea is that by providing a “guarantee” from a credit-worthy third-party that the MFI will repay both principal and interest (and if they are unable, the “guarantor” will meet the MFI’s obligations), the MFI should have the opportunity to gain access to both domestic and international funding sources which were previously unavailable. Ultimately, as the MFI proves its ability to repay and the commercial markets better understand the risks of the business, the relationship between the MFI and commercial markets should continue as the guarantee falls away.

Sufficiency (IFS): Reflects the ability of an MFI to cover all costs, including adjustments, which appear on the Income Statement in a given period with the adjusted operating income. The ratio is calculated as follows:

Labor Efficiency: Measures the efficiency and productivity of staff at your MFI, whose salary and salary-related expenses often represent between 50% to 70% of an MFI’s total administrative expenses. For purposes of this paper, we are focusing on two specific ratios used globally among MFIs, calculated as follows:

Leverage: Represents the ability of an MFI to use its equity as a “lever” to raise additional funds, in the form of liabilities (obligations to third parties) such as debt and savings deposits.

Liabilities: One of three primary accounts on the Balance Sheet. Represents obligations to third parties for which the MFI is “liable.” The most significant MFI liability accounts include both short-term and long-term debt financing and savings deposits.

Liquidity: Most often refers to the ability of an MFI to convert assets into cash in order to meet short-term cash obligations, if necessary. Can be a long-term measure as well.

Loan Loss Provisioning: The cost an organization incurs, recorded on the Income Statement, to maintain or build the value of the Loan Loss Reserve on the Balance Sheet.

Loan Loss Reserve: Represents the cumulative amount set aside in reserve by an MFI, via period to period loan loss provisioning, to protect against future loan losses. This account is reduced in value only when an MFI determines to write-off a loan.

Liabilities: Those assets and liabilities which do not represent monetary obligations. Fixed assets are an example of a non-financial asset.

Operating Self-Sufficiency: Reflects the ability of an MFI to cover all actual costs which appear on

(OSS) the Income Statement in a given period with the actual operating income. The ratio is calculated as follows:

Poor: Those households that fall in the top 50% below the official poverty line in a given country.

Poorest: Those households that fall in the bottom 50% below the official poverty line in a given country.

Portfolio-At-Risk Ratio: A measure of loan portfolio quality which considers not just missed repayments of delinquent clients, but the remaining outstanding balance of loans which are at risk of not being repaid. The determination of when a loan is at risk is based on the age of the arrears and can vary among MFIs. Prudent managers should use a “cut-off” of 30 days or four weeks, whichever is more appropriate. The ratio is calculated as follows:

Portfolio Yield: Measures the amount of income actually yielded from the loan portfolio. The difference between the APR and Portfolio Yield indicates how efficiently an MFI is collecting interest compared to expectations. The ratio is calculated as follows:

Quasi-Equity: Often referred to as subordinated debt. This funding comes into an MFI as equity and is repaid to lenders like debt. “Subordinated” refers to the position of lenders in the repayment hierarchy in the event of liquidation; they are subordinate to senior debt holders, but are repaid prior to equity holders.

Retained Earnings: Retained earnings reflect the accumulated annual profit or loss generated by an MFI, as calculated on the Income Statement. They are recorded as equity on the Balance Sheet.

References

Adams, Dale & others, eds. (1974). Undermining Rural Development with Cheap Credit. Boulder, Colorado: Westview Press.

Bresnick S. and MkNelly, B. (1999). Poverty Profile of CRECER Credit with Education Client Households. Unpublished paper provided by email communication from Freedom From Hunger.

Christen, R.P. (1997). Banking Services for the Poor: Managing for Financial Success (An Expanded and Revised Guidebook for Microfinance Institutions). ACCION International.

Christen R.P., Helms, B. and Rosenberg, R. (1997). CGAP Format for the Appraisal of Microfinance Institutions. Washington, DC: World Bank.

Christen, R.P., Rhyne, E., Vogel, R.C. and McKean, C. (1995). Maximizing the Outreach of Microenterprise Finance: An Analysis of Successful Microfinance Programs. USAID Program and Operations Assessment Report No.10. Washington, DC: USAID.

Conning, J. (1998). Outreach, Sustainability and Leverage in Microfinance Lending: A Contract Design Approach. Paper prepared for the NEUDC Conference, October 16 & 17, Economic Growth Center, Yale University.

Gibbons, D. (1999). Training Manual on Cost-effective Targeting. Seremban, Malaysia: CASHPOR Inc.

Gibbons, D. (1998a) Can the ‘Core Poor’ Benefit from Microcredit?. Credit for the Poor #20. Seremban, Malaysia: CASHPOR Inc.

Gibbons, David (1998b). Poverty-Reduction Can Be Good Business for Banks. Paper presented at the Workshop on "Kick-starting Microfinance: Challenges for Indian Banks", 26-28 Oct., at the Banker's Institute for Rural Development in Lucknow, India.

Gulli, H. (1998). Microfinance and Poverty: Questioning the Conventional Wisdom. Washington, DC: Inter-American Development Bank.

Harper, M . (1998). Profit for the Poor. London: Intermediate Technology Press.

Helms, B. and Mosley P. (1996). Financial Sustainability, Targeting the Poorest and Income Impact: Are There Trade-Offs for Micro-Finance Institutions. CGAP Focus Note No. 5. Washington, DC: World Bank.

Hossain, M. and Diaz C. (1997). Reaching the Poor with Effective Microcredit: Evaluation of a Grameen Bank Replication in the Philippines. Philippines: International Rice Research Institute.

Hulme, D. & Mosley P. (1996). Finance Against Poverty: Volume I. London & New York: Routledge.

Meehan, J. & Gibbons D. (1999). Tracking Financial and Operational Performance. Seremban, Malaysia: CASHPOR Inc.

Microfinance Network (1998). Moving Microfinance Forward. 5^th Annual Conference of the Microfinance Network.

Morduch, J. (June 1998). Does Microfinance Really Help the Poor?: New Evidence from Flagship Programs in Bangladesh. Unpublished.

Morduch, J. (August 1998a). The Grameen Bank: A Financial Reckoning. Unpublished.

Navajas, S. and others (1998). Microcredit and the Poorest of the Poor: Theory and Evidence from Bolivia. Paper submitted for presentation at the Latin American and Caribbean Economic Association, Oct.22-24, Universidad Torcuato Di Tella, Buenos Aires, Argentina.

Otero, M. & Rhyne E. (1994). The New World of Microfinance: Building Healthy Microfinance Institutions for the Poor. West Hartford, Connecticut: Kumarian Press.

Rhyne, E (1998). The Yin and Yang of Microfinance: Reaching the Poor and Sustainability. The Microbanking Bulletin ( No.2). Boulder, Colorado: Economics Institute.

Rock R., Salinger, D. and Saltzman, S. (1998). Performance and Standards in Microfinance: ACCION’s Experience with the CAMEL Instrument. Discussion Paper Series Document No. 7. ACCION International

Rosenberg, R. (1996). Microcredit Interest Rates. CGAP Occasional Paper No.1. Washington, DC: World Bank.

SEEP Network (1995). Financial Ratio Analysis of Microfinance Institutions. New York: Pact Publications.

Sheldon, T. & Waterfield, C. (1998). Business Planning and Financial Modeling for Microfinance Institutions: A Handbook. New York: Pact Publications.

Simanowitz, A. & Nakuna, B. (1998) Participatory Wealth Ranking Operation Manual: Version 3.0. Tzaneen, South Africa: The Small Enterprise Foundation.

Todd, H. (1996). Women at the Center: Grameen Bank Borrowers After One Decade. Boulder: Westview Press.

Waterfield, C. and Ramsing, N. (1998). Handbook for Management Information Systems for Microfinance Institutions. New York: Pact Publications.

[1] For the purpose of this paper, the 1997 Microcredit Summit, and the Summit’s nine-year fulfillment campaign, any reference to microcredit should be understood to refer to programs that provide credit for self-employment, and other financial and business services (including savings and technical assistance), to very poor persons.

[2] The authors would like to thank the Microcredit Summit for inviting them to write the paper and for extending full co-operation in the process. Rob Gailey of the Secretariat deserves special mention for his hard work on our behalf. The commitment of our case study MFIs to providing requested information in a timely and accurate manner, particularly given their commitments in the field, was very much appreciated. We would specifically like to thank Dolores Torres, Marjorie Bugia, Robert Ridgley, Stuart Bresnick, Barbara MkNelly, Beth Porter, John Hatch, Michael McCord, and Paul Ssegawa. Valuable comments were received from a large number of readers to whom an earlier draft was circulated by the Summit Secretariat. Particularly valuable comments were received from Sam Daley-Harris, Alex Counts, Charles Waterfield, Howard Brady, Gary Woller, Bill Gheen, and Hartmut Schneider. We thank all commentators for the time they have taken out of their busy schedules. We have done our best to incorporate your suggestions, and feel the paper is much stronger because of them. Helen Todd proofread the final draft and made valuable suggestions. Nevertheless, we take final responsibility for what we have written. Comments are welcomed by both authors whose e-mail addresses are as follows: gibbons@pc.jaring.my and jennifer_meehan@hotmail.com.

[3] All terms followed by a “*” symbol appear in the Glossary of Terms on pages 30-32.

[4] Here we are following the Microcredit Summit definition of the "poorest" families as those who live in households with incomes that place them in the bottom 50% of the poverty group as defined officially in each country. Households in the top 50% of a country's poverty group are termed "poor".

[5] Jonathan Conning (1998:2), referring to Christen,1997; Microfinance Network, 1998; and Otero & Rhyne, 1994.

[6] The inflation and subsidy adjustments are calculated based on the method outlined in Chapter 2, “Evaluating MFI Financial Sustainability,” of Christen’s Banking Services for the Poor: Managing for Financial Success.

[7] This is a very simplified assumption, which does not address the maintenance of principal value against inflation, among other considerations.

[8] In some cases, apparently unlimited amounts of subsidized funds for on-lending to the poor may be available in the form of refinancing as "priority sector lending" from central or apex banks. The conditions required to obtain such funding, however, are likely to be similar to those for commercial funding from formal financial institutions. Moreover, such subsidized lending may not be sustainable in poor economies.

[9] A rejoinder was submitted to CGAP for publication, but rejected on grounds that there were many other contributions awaiting publication that had to be dealt with first before a "new issue" could be published! See Gibbons (1998) “Can the ‘Core Poor’ Benefit from Microcredit” in CASHPOR’s newsletter Credit for the Poor, Issue No. 20.

[10] While we recognize that it is more costly to serve the rural poor and poorest, as compared to the urban poor and poorest, it can be done in a financially sustainable manner.

[11] We have been asked why we did not choose the better-known MFIs, like BancoSol, K-Rep, BRI, and Grameen Bank as case studies. The answer is that the first three do not meet one of our key selection criteria: working with a substantial number of the poorest. Neither BancoSol, nor K-Rep, nor BRI deal with substantial numbers of the poorest households in their countries. The Grameen Bank, of course, has many clients among the poorest households in Bangladesh, but we thought our case would be stronger if demonstrated with a replication/adaptation. In this way we can avoid the “sui generis” argument.

[12] E-mail communication from John Hatch, Founder and Director of Research, FINCA, 8 March 1999.

[13] The failure of the Co-Op Bank was very difficult for FINCA Uganda, and it is to their credit that they were able to stabilize the institution in such short order. Nevertheless, it has a significant impact on all 31/12/99 results. When the Co-Op Bank closed, the clients’ first instinct was that FINCA Uganda was also “closed.” However, FINCA Uganda got out and spoke to their clients, reinforcing that it was business as usual. As not all of FINCA Uganda’s funds were at the Co-Op Bank, and they were also able to get new commercial funds (with guarantees from the Bank of Uganda backed by another guarantee from FINCA International), they were able to disburse all subsequent loans, thus maintaining the confidence of their clients. FINCA Uganda did not require cash payment for scheduled repayments held up with the bank; instead, the clients were required to sign a deed of assignment.

[14] It is important to note that as the competitive environment among MFIs increases worldwide, as is currently being experienced in Bangladesh and many parts of Latin America, MFIs will be forced to reduce interest rates in order to retain their clients and remain competitive. Under such conditions, the impact on the long-term viability of inefficient and poorly managed institutions would be devastating.

[15] Increasingly, efforts are being made globally to compile comparable operational and financial data on MFIs. One of the most comprehensive examples of this so far is the Microbanking Bulletin, produced originally by the Microfinance Program at the Economics Institute, Boulder, CO and now by Calmeadow, which is distributed biannually. Efforts to develop rating agencies for MFIs, such as Private Sector Initiatives and EDA Rural Systems, and independently developed analyses, such as ACCION’s CAMEL, will also contribute to these efforts going forward.

[16] A separate paper at the Microcredit Summit Campaign’s 1999 Meeting of Councils in Abidjan will address this topic in significant detail.

[17] For more information, please refer to the manual Cost-Effective Targeting: Two Tools to Identify the Poor by David Gibbons and Anton Simanowitz with Ben Nkuna. CASHPOR: Seremban, Malaysia [Fax(606)7642307 & e-mail: gibbons@pc.jaring.my]

[18] Any local currency unit can be included here. In cases where 1 unit of a currency is not a meaningful measure, 100 units or 1000 units (any multiple of ten) can be used.

[19] E-mail communication from Chuck Waterfield, March 26, 1999.

[20] For more details, please refer to the CGAP’s Handbook for Management Information Systems for Microfinance Institutions, February 1998, prepared by Nick Ramsing and Chuck Waterfield.

[21] See the final version of the CGAP’s Format for the Appraisal of Microfinance Institutions, 1997, the SEEP Network’s paper on "Financial Ratio Analysis for Microfinance Institutions", 1995, and CASHPOR’s recently revised manual Tracking Financial and Operational Performance (1999).

[22] For guidelines on calculating portfolio at risk and using it to determine the appropriate loan loss provision, see CGAP (1997) and Christen (1997).

[23] This is understood by changing assumptions – such as amount of funding, administrative costs, etc. – to gauge their impact on other key indicators, such as efficiency, OSS, and IFS. This is often called “variance” or “sensitivity” analysis.

[24] See, for example, the new CGAP Business Planning and Financial Modeling for Microfinance Institutions: A Handbook, November 1998, prepared by Tony Sheldon and Chuck Waterfield.

[25] The 5-Year Business Plan is not of course fixed for a period of five years. It will have to be updated and amended several times in light of varying actual experience during the period. The updated financial model will show the likely impact of such changes on the triple goals of maximizing the delivery of financial services to the poor in an efficient and financially sustainable way.

[26] "High risk" members would be the non-poor or those known in the village not to be trustworthy in matters of money.

[27] Earning enough additional income to put the household above the official poverty-line income.

[28] For fuller development of this point see Gibbons (1998b).

[29] The authors are grateful to Ismail Serageldin, Vice President of the World Bank, for ensuring this section and the following two sections were included in the paper.

[30] Robert Ridgley of CRECER pointed out in a March 11, 2000 email correspondence that in 1999, CRECER “…pursued poverty reduction at the expense of IFS. CRECER has been able to do this because it was able to generate enough additional financing in the form of both commercial funding and subsidy to permit continued program expansion…If CRECER had chosen not to continue program expansion into new areas (that are poor), then it could have probably reached and exceed 100% IFS by increasing its average loan size without a corresponding increase in operating costs.”

[31] The authors are grateful to Huguette Labelle, former President of the Canadian International Development Agency, for bringing this danger to their attention.

[32] We are grateful to Howard Brady of Global Partnerships in Seattle for specifically recommending this analysis be included here. It has greatly added to the strength of the paper.

[33] Director of the Social Science Division at the International Institute for Rice Research (IRRI), Los Banos, Philippines. (Hossain & Diaz, 1997)

[34] We have been informed of a tagalog saying among the poor in the Philippines that, "In extreme need, one will hold on even to the sharp edge of a knife," but do not think it to be relevant to microcredit for the poor. What is convincing about the near perfect repayment rates in microfinance for the poor is their persistence over decades which can be taken as evidence that they are benefiting from the opportunity. Other wise how would they be able to repay in full so consistently?

[35] CRECER is currently working to link its village banking groups up with formal sector financial institutions in order to provide a secure savings facility, which in the current regulatory environment in Bolivia CRECER itself cannot provide. This is currently in the experimental stage and will primarily serve those community banks that are close enough to formal financial institutions to make it worthwhile.

[36] The Grameen Bank, which is majority owned by its clients, shows that this is possible.

*Basic Statistics at 31/12/99 (in US$)*	CARD	CRECER	FINCA
Country of Operation	Philippines	Bolivia	Uganda
Inflation Rate	5.5%	3.1%	10%
Exchange Rate (end of period)	40.3 Pesos/US$	6.0 Bol./US$	1,520 Shil./US$

Lending Methodology	Grameen Replicator	Village Banking	Village Banking
Gross Loan Portfolio	US$3,691,622	US$2,825,387	US$1,131,069
Number of Loans Outstanding	44,341	17,353	20,769

Equity	US$1,901,555	1,567,682	1,691,306
Operating Profit/(Loss)	US$24,928	(69,026)	42,060
Total Staff	281	103	94

Operating Self-sufficiency*	102.2%	93.8%	105.5%
Financial Self-sufficiency (IFS)*	95.9%	85.0%	79.7%
Effective Interest Rate*	42.8%	42.0%	87.0%
Administrative Efficiency*	30.6%	38.5%	76.3%
Portfolio-At-Risk*	0.0%	0.2%	1.9%

*As of 31/12/98*	CARD	CRECER	FINCA
Active Savers	40,367	19,351	20,769
Poorest	Approx. 50%	49%	67%
Poor	Approx. 50%	34%	22%
Non-Poor	0%	17%	10%

Active Loan Clients	28,531	17,353	20,769
% Women	100%	98%	100%

Maximum First Loan Size	US$74.4	US$83.3	US$98.7
Maximum Initial Loan Term	6 months	16 weeks	16 weeks

Average Loan Outstanding per Loan Client	US$83.3	US$163.0	US$54.0
Average Savings Balance per Saver	US$42.6	US$29.1	US$65.0
No. of Years in Operation	10	6	7

Performance Indicator	Actual Performance	Planned Target	% Achievement
Active Savers	5,842	6,027	97%
Active Loan Clients	4,157	4,865	85%

Operational Self-Sufficiency	16%	16%	100%
Institutional Financial Self-Suff.	14%	14%	100%

Total Loans Outstanding (US$)	151,998	188,980	80%
Portfolio-At-Risk (> 28 Days)	8%	7%	114%