Microfinance

The Impact and Outreach of Microfinance Institutions: The Effect of Interest Rates

by

Sebastian Schwiecker (sebastian.schwiecker {at} helpedia {dot} org) www.helpedia.org

Revised November 2004

This paper was originally submitted in part fulfilment of the requirements for the degree of Diplom-Volkswirt (German equivalent to Master in Economics) from the University of Tübingen in October 2004.

Contents Contents........................................................................................................ 1 Figures .......................................................................................................... 3 Acronyms ..................................................................................................... 4 1. Introduction ............................................................................................. 5 2. The Development of Microfinance ........................................................ 7 2.1. What is Microfinance? ....................................................................................................................7 2.2. The Origin of Microfinance ............................................................................................................8 2.3. Microfinance after the Second World War.....................................................................................9 2.4. Microfinance Today.......................................................................................................................11

3. The Impact of Microcredit ................................................................... 13 3.2. The Eradication of extreme Poverty and Hunger.........................................................................13 3.3. Achievement of universal primary Education ..............................................................................15 3.4. Promote gender Equality and empower Women...........................................................................16 3.5. Reduce Child Mortality and improve Maternal Health................................................................17 3.6. Combat HIV/AIDS, Malaria and other diseases ..........................................................................18 3.7. Ensure environmental Sustainability............................................................................................20 3.8. Develop a global Partnership for Development ............................................................................21

4. Microcredit Outreach ........................................................................... 24 4.1. The Demand for Microcredit.........................................................................................................24 4.2. The Supply of Microcredit .............................................................................................................25 4.3. Meeting the Demand......................................................................................................................26 4.4. Microfinance and the Capital Markets .........................................................................................29

5. Credit Rationing.................................................................................... 31 5.1. The imperfect Information Paradigm ...........................................................................................31 5.2. Adaptation to the Microfinance Sector .........................................................................................33 5.3. Overcoming Credit Rationing for the Microfinance Sector .........................................................35

6. Interest Rates ......................................................................................... 41 6.1. Setting the right Interest Rate........................................................................................................41 6.2. Can Micro-Entrepreneurs bear these Rates..................................................................................43 6.3. Should the Interest Rate be subsidized anyway ............................................................................44

7. Empirical verification ........................................................................... 46 7.1. Setting up a Sample .......................................................................................................................46

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7.2. Examining the Sample...................................................................................................................47

8. Conclusions ............................................................................................ 52 Appendix .................................................................................................... 54 Appendix 1: Average loan balance.......................................................................................................54 Appendix 2: Portfolio at Risk ...............................................................................................................56 Appendix 3: Transaction Costs ............................................................................................................58 Appendix 4: Portfolio Growth ..............................................................................................................60 Appendix 5: Portfolio vs. Equity ..........................................................................................................60 Appendix 6: Internal Rate of Return ...................................................................................................61 Appendix 7: Subsidized Interest Rates and the Net Gains to Society..................................................62 Appendix 8: Interest Rate vs. Loan Size vs. FSS vs. PAR. ..................................................................63 Appendix 9: Inflation vs. GDP per Capita ...........................................................................................65

References .................................................................................................. 67 Internet References ................................................................................... 71 Ratings........................................................................................................ 76 Other Sources ............................................................................................ 78

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Figures Figure 1: Expected Bank Return and the Interest Rate ........................................... 32 Figure 2: The extended Model..................................................................................... 34 Figure 3: Marginal Productivity of Capital ............................................................... 36 Figure 4: The extended Model..................................................................................... 39 Figure 5: Loan Size vs. Interest Rate.......................................................................... 48 Figure 6: Interest Rate vs. PAR .................................................................................. 49 Figure 7.: Interest Rate vs. FSS................................................................................... 50

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Acronyms BDB

Bank Dagan Bali

BRAC

Bangladesh Rural Advancement Committee

BRI

Bank Rakyat Indonesia

CF

Cost of Funds

CGAP

Consultative Group to Assist the Poor(est)

FSS

Financial Self-Sufficiency

GDP

Gross Domestic Product

GNP

Gross National Product

I

Interest Rate

IADB

Inter-American Development Bank

II

Investment Income

IMI

Internationale Mikro Investitionen Aktiengesellschaft

IPCC

Intergovernmental Panel on Climate Change

K

Capitalization Rate

LL

Loan Loss

MBB

MikroBanking Bulletin

MDG

Millennium Development Goal

MFI

Microfinance Institution

NGO

Non Governmental Organization

NGS

Net Gains to Society

PAR

Portfolio at Risk

TA

Transaction Costs

UN

United Nations

USA

United States of America

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1. Introduction "The poor stay poor, not because they are lazy but because they have no access to capital."1 If this claim made by nobel laureate Milton Friedman actually holds will be discussed in the initial part of this paper.

First a short overview of the development of the microfinance sector will be given, showing that the concept of providing financial services to low income people is much older than still believed by many development practitioners and bankers around the world. This is important since it underlines the contribution that microfinance institutions (MFIs) can make to the development of the financial sector in their respective countries. Subsequently the various ways how such services can assist low income people will be discussed demonstrating that even the poorest can benefit from the provision of small loans. This is a view that is still questioned in the academia.

In the second part of this paper it will be shown that although some 10,000 organizations are involved in microfinance already,2 they are not even close to meet the tremendous demand for low scale financial services. This sector is still widely neglected by for profit investors and traditional bankers. After examining why these groups continue to avoid investments in the microfinance industry it will be checked if the assumptions that underline their arguments actually hold. The possibility if MFIs could raise their interest rates to an extend that would allow them generate profits sufficiently high to attract for profit investors will be discussed.

According to Hulme and Mosley 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”.3 If this is true will be examined in the third part by checking if poor borrowers are able to bear interest rates significantly above 1

See Friedman (n.d.). See BlueOrchard (2004a). 3 See Hulme and Mosley (1996), p. 206. 2

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those charged by conventional banks. Finally a sample of 39 MFIs will be used to verify the drawn conclusions, by examining the effects their interest rates have on their financial performances and their repayment record.

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2. The Development of Microfinance 2.1. What is Microfinance?

According to the Journal of Microfinance the term defines what “is arguably the most innovative strategy to address the problems of global poverty“.4 This view is shared by the United Nations (UN) which declared that the year 2005 would be the international year of microcredit,5 while their General Secretary Kofi Annan stated in 2002, that “[m]icrocredit is a critical anti-poverty tool and a wise investment in human capital.”6

Hossain describes microfinance as “the practice of offering small, collateral free loans to members of cooperatives who otherwise would not have access to the capital necessary to begin a small business or other income generating activities.”7 This view is to narrow, since it not only excludes such services as saving accounts and insurances,8 but also ignores the possibility of collateral demanding MFIs. Although it is true that many MFIs do not take collateral, especially if they are focusing on the poorest which normally do not possess any collateral,9 several MFIs in fact do require some form of collateral.10

Roth and Steinwand give a more general definition. They describe microfinance as “the provision of a wide range of financial services like saving accounts, loans, payment services and insurances for people with no regular access to financial services through traditional financial institutions.”11 Here it is important not to confuse the terms traditional or conventional with the term formal. If, for example, an illiterate beggar takes a $10 loan from the Grameen Bank in Bangladesh she becomes a client of a

4

See Woodworth and Woller, (1999), p. 6. See United Nations (1999), p. 1. 6 Annan (2002). 7 Hossain (2004). 8 See The Microfinance Gateway (n.d); Robinson (2001), p.9; Otero (1999), p. 8. 9 See chapter 3 for a more detailed discussion on poverty. 10 See Robinson 2002, p. 243. 11 See Roth and Steinwand (2004), p. 2 “die Bereitstellung einer breiten Palette von Finanzdiensteistungen wie Spareinlagen, Kredite, Zahlungsverkehr und Versicherungsleistungen für Wirtschaftsakteure, die keinen regelmäßigen Zugang zu Finanzdienstleistungen durch klassische Finanzinstitutionen haben“. 5

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formal financial institution (a registered bank), but obviously not of a traditional or conventional one.12

Although the other services have become more important in recent years, microcredit is still considered their central service by most MFIs. In many countries it refers to loans below $ 1,000,13 however for significant cross country comparison it is more meaningful to look at the ratio of the loan amount to the gross national product (GNP) or gross domestic product (GDP) per capita for the respective countries. While a loan of $1,000 is more than four times larger than the GNP per capita in Uganda, it represents less than 20% of the GNP per capita in Mexico. The MicroBanking Bulletin (MBB) has categorized the target market for MFIs by the average outstanding loan amount as a percentage of the GNP per capita ranging from Low-end (less than 20%) to Small Business (more than 250%).14 Especially for the latter category one must to look closely at the condition of the financial service market where an institution is active. Although MFIs exist in industrialized countries,15 one would definitely turn to a traditional financial institution for a loan bigger than 250% of the GNP per capita and therefore not to a MFI according to the definition presented above.

2.2. The Origin of Microfinance

Although neither of the terms microcredit or microfinance were used in the academic literature nor by development aid practitioners before the 1980s or 1990s,16 respectively, the concept of providing financial services to low income people is much older. While the emergence of informal financial institutions in Nigeria dates back to the 15th century,17 they were first established in Europe during the 18th century as a response to the enormous increase in poverty since the end of the extended European wars (1618 –

12

See Yunus (2002), p. 19 for a more detailed description of the destitute member program of the Grameen Bank which focuses exclusively on persons with no regular income and no assets at all. 13 See MicroBanking Bulletin (1997), p. 6. 14 See MicroBanking Bulletin (2003), p. 54. 15 See Yunus (1998a), pp. 229 et sqq. 16 See Robinson (2001), p. XXX. 17 See Seibel (2003a), p. 12.

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1648).18 In 1720 the first loan fund targeting poor people was founded in Ireland by the author Jonathan Swift.19 After a special law was passed in 1823, which allowed charity institutions to become formal financial intermediaries a loan fund board was established in 1836 and a big boom was initiated. Their outreach peaked just before the government introduced a cap on interest rates in 1843. At this time, they provided financial services to almost 20% of Irish households.20 The credit cooperatives21 created in Germany in 1847 by Friedrich Wilhelm Raiffeisen served 1.4 million people by 1910.22 He stated that the main objectives of these cooperatives “should be to control the use made of money for economic improvements, and to improve the moral and physical values of people and also, their will to act by themselves.”23

In the 1880s the British controlled government of Madras in South India, tried to use the German experience to address poverty which resulted in more than nine million poor Indians belonging to credit cooperatives by 1946.24 During this same time the Dutch colonial administrators constructed a cooperative rural banking system in Indonesia based on the Raiffeisen model25 which eventually became Bank Rakyat Indonesia (BRI), now known as the largest MFI in the world.26

2.3. Microfinance after the Second World War

Because of further prudential regulation and effective supervision, e.g. through bank superintendencies, the banking sector in the now developed world experienced continuous growth. Today the vast majority of the citizens of the industrialized countries have access to financial services with many of them being customers of former MFIs.27

18

See Steinwand (2001), p. 51. See Robinson (2002), p. 96. 20 See Seibel (2003a), p. 10. 21 first known as Darlehnsvereine and now called Raiffeisenbanken 22 See Morduch (1999a), p. 1573. 23 Raiffeisen (1966), quoted in Richardson (2000), p. 3. 24 See Morduch (1999a), p. 1574. 25 See Robinson (2002), p. 97. 26 See Roth, Steinwand (2004), p. 3. 27 See Seibel (2003a), p. 11. 19

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This is not true for most developing countries where market penetration of financial institutions is far from reaching the majority of the inhabitants (see chapter 4). A variety of reasons is responsible for this, including that the banking sector in the most developing countries have developed in a very different way to than those in industrialized countries.

One of the most important reasons is that, in contrast to the industrialized countries little attention has been paid to the legal recognition, prudential regulation and effective supervision of informal financial institutions.28 This insecure legal environment leads to several problems. Since the regulatory framework for mobilizing savings often does not fit the needs of institutions targeting low income people, many organizations are not permitted to do so and are therefore missing funds to on-lend as credit.29 An additional problem is the lack of a deposit insurances. This not only increases the risk of a bank run, but also keeps people from depositing their savings in a financial institution.30 In addition, inappropriate interest rate ceilings prevail in many countries. While politics claim they are lowering the interest rates charged to the poor, they de facto only limit their access to financial services (see chapter 5).31

Another problem that retarded the growth of the financial sector in developing countries was the supply-leading finance theory which dominated the development strategies of many countries after the second world war.32 This theory “ refers to the provision of loans in advance of the demand for credit, for the purpose of inducing economic growth.”33 Since it was believed that poor people were neither able to save (and thus dependent on outside funding), nor capable of paying commercial rates of interest, massive subsidized credit programs were established.34 The results were alarming. Repayment rates often did not exceed 50 percent and some government financed programs even had default rates of more than 90 percent.35 The main reason being that credit was not provided according to business management principles but instead 28

See Seibel (1998), p. 8. See Robinson (2001), p. 49. 30 See Staschen (1999), p. 14. 31 See Gibbons (2000), p. 15. 32 See Morduch (1999a), p. 1570. 33 Robinson (2001), p. 140. 34 See Borst (2004), pp. 33 et sqq. 35 See Yunus (1998a), p. 154; Robinson (2001), p. 145. 29

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according to political objectives. Therefore receiving loans was seen as a reward for the constituents rather than as a business transaction. Most of the funds earmarked for the poor also never reached them ending up instead in the hands of the local elites. Attracted by the below market interest rates and the possibility not to pay back at all, these groups lobbied to be treated privileged and often used the money received for consumption or for on-lending at higher rates.36 But subsidized credit not only encouraged corruption, it also discouraged sustainable financial institutions, since they could not compete with interest rates that were not aimed to cover all costs and were sometimes even lower than the interest paid on savings.37 The low or negative spread also worked as a negative incentive to institutions’ effort to mobilize savings which in turn intensified the problem of credit rationing (see chapter 5).

2.4. Microfinance Today

In the 1970s a paradigm shift started to take place. The failure of subsidized government or donor driven institutions to meet the demand for financial services in developing countries let to several new approaches. Some of the most prominent ones are presented below.

Bank Dagan Bali (BDB) was established in September 1970 to serve low income people in Indonesia without any subsidies and is now “well-known as the earliest bank to institute commercial microfinance”.38 While this is not true with regard to the achievements made in Europe during the 19th century, it still can be seen as a turning point with an ever increasing impact on the view of politicians and development aid practitioners throughout the world. In 1973 ACCION International, a United States of America (USA) based non governmental organization (NGO) disbursed its first loan in Brazil39 and in 1974 Professor Muhammad Yunus started what later became known as the Grameen Bank by lending a total of $27 to 42 people in Bangladesh.40 One year later the Self-Employed Women’s Association started to provide loans of about $1.5 to 36

See Robinson (2001), pp. 144 et sqq. Before BRI underwent a major transformation in 1983 a negative spread between interest rates on loans and savings existed. While the bank lent at a 12% annual effective interest rate, it paid 15% on savings. See Robinson (2001), p. 106. 38 Wardhana (2001), p. XXVII. 39 See ACCION (n.d.). 40 See Yunus (1998a), p. 16 et sqq. 37

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poor women in India.41 Although the latter examples still were subsidized projects, they used a more business oriented approach and showed the world that poor people can be good credit risks with repayment rates exceeding 95%,42 even if the interest rate charged is higher than that of traditional banks. Another milestone was the transformation of BRI starting in 1984. Once a loss making institution channeling government subsidized credits to inhabitants of rural Indonesia it is now the largest MFI in the world, being profitable even during the Asian financial crisis of 1997 – 1998.43

In February 1997 more than 2,900 policymakers, microfinance practitioners and representatives of various educational institutions and donor agencies from 137 different countries gathered in Washington D.C. for the first Micro Credit Summit. This was the start of a nine year long campaign to reach 100 million of the world poorest households with credit for self employment by 2005.44 According to the Microcredit Summit Campaign Report 67,606,080 clients have been reached through 2527 MFIs by the end of 2002, with 41,594,778 of them being amongst the poorest before they took their first loan.45 Since the campaign started the average annual growth rate in reaching clients has been almost 40 percent.46 If it has continued at that speed more than 100 million people will have access to microcredit by now and by the end of 2005 the goal of the microcredit summit campaign would be reached. As the president of the World Bank James Wolfensohn has pointed out, providing financial services to 100 million of the poorest households means helping as many as 500 – 600 million poor people.47

41

See Bhatt (1997). See Yunus (1998a), p. 143 et sqq. 43 See Robinson (2002), p. 380. 44 See Daley-Harris (2003), p. 3. 45 “The Microcredit Summit Campaign defines “poorest” as those who are in the bottom half of those living below their nation’s poverty line or are living on less than $1 a day, adjusted for purchasing power parity (PPP)” See Daley-Harris (2003), p. 3. This definition will also be used throughout this paper. 46 See Daley-Harris (2003), p. 18. 47 See Morduch (1999a), p. 1570. 42

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3. The Impact of Microcredit

3.1. Microcredit and the Millennium Development Goals

In 1996 Ismail Serageldin, the then chair of the Consultative Group to Assist the Poorest (CGAP)48 claimed that “[m]icrofinance is a proven instrument to assist the very poor.”49 This view is also shared by the World Watch Institute50 and underlines what has been stated above, namely, that microfinance can be used as a strong anti-poverty tool. But does microfinance actually meet these expectations and keep this promise?51

The impact that microcredit has on the lives of poor people will be demonstrated by showing how it contributes to each of the eight Millennium Development Goals (MDGs),52 which are related to the reduction of poverty in all its forms. These goals were resolved in September 2000 by the General Assembly of the UN in their Millennium Declaration.53

3.2. The Eradication of extreme Poverty and Hunger

The aim of the first MDG is to halve the proportion of people who live on less than $1 a day between 1990 and 2015. According to current population projections of the World Bank,54 this would equal 849 million people in 2015.55

48

The Consultative Group to Assist the Poorest is a consortium of 28 public and private development agencies working together to expand access to financial services for the poor in developing countries and was established by the World Bank in 1995. Recently it changed its name to Consultative Group to Assist the Poor. 49 See Serageldin (1996), p. 11. 50 See Gardner (2002), p. 18. 51 See Morduch (1999a), pp. 1569 et sqq. 52 The Millennium Development Goals are: (1) eradicate extreme poverty and hunger; (2) achieve universal primary education; (3) promote gender equality and empower women; (4) reduce child mortality; (5) improve maternal health; (6) combat HIV/AIDS, malaria, and other diseases; (7) ensure environmental sustainability; and (8) develop a global partnership for development. 53 See United Nations (2000). 54 See World Bank (2004), p. 38 et sqq. 55 Although it is not explicitly mentioned in the Millennium Declaration, it is commonly assumed that the share of people living on less than $1 in low and middle income countries should be halved. If the share would be calculated with regard to the world’s total population, the goal for 2015 would 818 million people. For the world’s total population in 1990 see U.S. Census Bureau (2004).

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Today almost 1.1 billion people live on less than $1 and more than 2.7 Billion live on less than $2 per day.56 The mean income for those living under $1 a day is $0.77 and it is $1.25 for those living under $2 a day.57 While the number of people living on less than $1 per day is slowly decreasing and is expected to be 913 million in 2015, the number of people living on less than $2 a day is actually increasing.58 One of the outcomes from this degree of poverty is that more than 27,000 children die every day from mostly preventable diseases, many of them related to hunger.59

Microcredit enables poor people to start new businesses, or to diversify existing ones, which helps them to increase their income. In addition to large amounts of anecdotal evidence, several studies have documented the positive economic impact microcredit has on its clients. Cloud and Panjaitan-Drioadisuryo have found that the average income of a sample of BRI borrowers has increased by 112 percent and that 90 percent of their families have crossed the poverty line.60 Only 12 out of the 121 respondents of that study reported that their income had not increased.61 According to the Grameen Bank website, 51.09 percent of their clients have crossed the poverty line since they took their first loan.62

A study conducted by Barnes showed that the program of the Zambuko Trust, a local MFI in Zambia, had a considerable impact on the poorest clients consumption of meat, fish and chicken.63 While 76 percent of the continuing clients of the MFI called Share in India significantly increased their income and 50 percent have crossed the poverty line, 64

Khandker discovered in his well known study “Fighting poverty with microcredit”,

that there is a connection between the microcredit programs of three Bangladeshi MFIs and household per capita expenditure. For every dollar borrowed by a Grameen Bank member, household consumption increases by $0.18. In addition he found that 5 percent

56

See Chen and Ravallion (2004), p. 29. In fact those people live on less than $1.08 and $2.15 respectively a day in 1993 PPP. This means that the nominal dollar value of their income is considerably lower. See Chen and Ravallion (2004), p. 9. 58 Both numbers are decreasing in Asia but increasing in the rest of the world. See Chen and Ravallion (2004), p. 22 et sqq. 59 See UNICEF (2002), p. 6. 60 A consistent definition of the term “poverty line” does not exists among different nations Therefore the term will be used according to the definition of the respective country in this paper. 61 See Panjaitan-Drioadisuryo and Cloud (1999), p. 775. 62 See Grameen Bank (2004). 63 See Barnes (2001), p. 86. 64 See Simanowitz and Walter (2002), p. 16. 57

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of Grameen Bank borrowers move out of poverty every year.65 Supposing Khandker’s findings were correct for all MFIs worldwide and the Microcredit Summit Campaign achieved its goal of reaching 100 million of the poorest households with microcredit by 2005, then 25 million people (assuming an average family size of five) would leave poverty every year.66 Although this number would have to be adjusted in order take into consideration the MFI clients who have crossed the poverty line already, and of course the experiences made in Bangladesh can not be transferred precisely to other countries, it still demonstrates the enormous effect that microcredit can have on assisting the poorest to move out of poverty.

Although a 1996 study of Hulme and Mosley states that providing microcredit can also have a negative impact on the income of the poorest households,67 these findings can be considered as disproved by the recent bulk of evidence drawing favorable conclusions.68

3.3. Achievement of universal primary Education

The aim of the second MDG is to ensure that all children will be able to complete primary school by 2015. Currently more than 100 million children, most of them girls, are not in school.69 Besides school fees, poor households often can not afford to send their children to school because they need them to help in the family business.

Microcredit programs mainly contribute to the second MDG by increasing families income and therefore enabling parents to send their children to school. Barnes, Gaile and Kibombo found that clients of an MFI in Uganda spent significantly more on children’s education than non-clients,70 and in Zambia Barnes showed that ongoing participation in the Zambuko Trust’s program had a positive impact on the members

65

See Khandker (1998), p. 56. See Daley-Harris (2003), p. 24. 67 See Hulme and Mosley (1996), p. 180 et sqq. 68 See Gibbons and Meehan (1998), p. 136. 69 See UNICEF (2002), p. 22. 70 See Barnes, Gaile and Kibombo (2001), p. 64. 66

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children staying in school.71 In Bangladesh, the probability of girls enrolling in school increased by 1.9 percent for every 1 percent increase in Grameen Bank loans to female clients. For boys this number was even higher with the mean being 2.4 percent.72

3.4. Promote gender Equality and empower Women

The aim of the third MDG is to eliminate the disparity in the ratios of girls to boys in primary, secondary and tertiary education, in the ratio of literate females to males and to increase the share of women in the labor market and in parliament.73

At present the literacy rate for women is lower than for man all over the world. According to the Human Development Report 2004, the average female literacy rate as a percentage of male literacy rate is 75.9 in developing countries and is as low as 37 in Niger.74 But women are not only discriminated against in education but also in access to medical services and therefore have a higher mortality rate than man.75 The worldwide tendency for women to be underrepresented in the labor market and in the parliaments is even more severe in developing countries.76 Although several women have become heads of state in various Asian countries (e.g. Bangladesh, India and Indonesia) this is probably due to them being wives or daughters of former respected male politicians. Since women have a higher repayment rate than man77 and also tend to invest increases in income on the household and on their children rather than on themselves,78 most MFIs prefer female clients.79 Therefore they empower women in many ways. Often MFIs not only encourage women to become involved in financial transactions for the first time, but also to become independent, active members of the economy and to acquire assets in their own names. Cheston and Kuhn found that 68 percent of the women participating in an MFI program in Nepal “… experienced an increase their

71

See Barnes (2001), p. 84. See Khandker (1998), p. 49. 73 See World Bank (2001), p. 20. 74 See UNDP (2004), pp. 225 et sqq. 75 See Sen (2000), p. 232. 76 See Sen (2000), pp. 242 et sqq. 77 See Littlefield, Morduch and Hashemi (2003), p. 7. 78 See Yunus (1998a), pp. 116 et sqq. 79 According to the Microcredit Summit Campaign Report 79%, or 37,677,080 of the poorest clients were women. See Daley-Harris (2003), p. 3. 72

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decision-making roles in the areas of family planning, children’s marriage, buying and selling property, and sending their daughters to school - all areas of decision making traditionally dominated by men.”80 The program of the Lower Pra Rural Bank in Ghana had an positive impact on women’s participation in the community81 which is true as well for the CRECER program in Bolivia.82 In Bangladesh the Grameen Bank and the Bangladesh Rural Advancement Committee (BRAC), the two biggest MFIs of the country had a significant positive effect on women’s empowerment measured through eight different indicators.83 After joining Grameen Bank only 21 percent of the female members considered themselves as unemployed, while this number was 50 percent before joining.84 According to the Grameen Bank website, 93 percent of the Bank’s shares are owned by their borrowers which elect the members of the board every three years. They also get familiar with the election process by more frequently voting for representatives at the lower level of the organizational structure. Partly because of this experience, they are more likely to run for public office. In the 2003 local government elections Grameen Bank members constituted 24% of the seats reserved for women, while they represent less than 10% of Bangladeshi adult female population.85 Female clients of MFIs from countries like the Philippines, Bolivia and Nepal have reported similar occurrences.86

3.5. Reduce Child Mortality and improve Maternal Health

The aim of the fourth MDG is to reduce the under-five mortality rate by two-thirds between 1990 and 2015 and the fifth MDG strives for a three quarter reduction in the maternal mortality ratio by the same date.

As has been stated above, more than 10 million children are dying annually in the developing world of mostly preventable causes with malnutrition playing a role in over

80

See Cheston and Kuhn (2002), p. 18. See MkNelly and Dunford (1998), p. 54. 82 See MkNelly and Dunford (1999), p. 71. 83 The eight different indicators are: “freedom of movement, economic security, ability to make small and larger independent purchases, participation in important family decisions, relative freedom from domination by the family, political and legal awareness, and participation in political campaigning and public protests.” Hashemi and Schuler (1998), p. 36. 84 See Holcombe (1995), p. 51. 85 See Grameen Bank (2004)and CIA (2004). 86 See Littlefield, Morduch and Hashemi (2003), p. 8. 81

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half of the child’s deaths and in over 50 countries child mortality rates are greater than 100 deaths per 1,000 live births.87 Maternal mortality is also a significant problem in the developing world with more than 500,000 women dying every year from pregnancy related causes.88

MFIs are mainly contributing to the these MDGs by increasing families income and therefore enabling the households to afford better nutrition and medical services. Furthermore, they are often linked with microinsurance or health education programs. In addition to what has been showed in chapter 3.1., Pitt, Khandker and Millimet estimate that a 10 percent increase in the loan size of a female borrower in Bangladesh increases the arm circumference of their daughters by 6.3%.89 Smith and Jain found that village banking in Ecuador had a positive effect on child diarrhea90 which is responsible for 15% of child deaths.91 Since Sen argues that an improved status of women within the family reduces child mortality,92 the empowerment of women through MFIs is also contributing to this cause.

Although no study has been conducted so far on the direct impact of microcredit on maternal health, it can be assumed that an overall improvement of access to medical services reduces maternal mortality as well. A study of the Morgan State University, for example, suggests that microcredit has a positive effect on the prevalence of contraceptives93 which leads to a decrease of unwanted pregnancies and therefore reduces maternal death.

3.6. Combat HIV/AIDS, Malaria and other diseases

The aim of the sixth MDG is to halt and begin to reverse by 2015 the spread of HIV/AIDS, malaria and other major diseases and to assist children orphaned by HIV.

87

See World Bank (2001), p. 8. See WHO (2004) . 89 See Pitt, Khandker and Millimet (2003), p. 113. 90 See Smith and Jain (1999), p. 37. 91 See WHO (2001). 92 See Sen (2000), p. 235. 93 See Amin et al. (2001), p. 1614. 88

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In 2003 2.9 million people died because of HIV and about 4.8 million became infected, the greatest number since AIDS was discovered 23 years ago.94 This pandemic has lead to a tremendous decrease in the life expectancy in the hardest hit countries. Botswana for example, one of the richest and most democratic countries in Africa, has a HIV/AIDS adult prevalence rate of 38.8% and a life expectancy at birth of less than 31 years.95

Malaria not only causes more than one million deaths annually, it leads also to 300 – 500 million clinical cases a year. Both numbers have been steadily increasing over the past two decades.96 More than two million people a year are killed by tuberculosis, another global health problem. This curable disease is estimated to cost poor communities about $12 billion every year, since even those who survive normally lose more than 3 month of work time while recovering.97

MFIs contribute to the sixth MDG in several ways. By increasing client’s income, they help households affected by HIV or other diseases to better cope with the situation. Besides spending more money on health services and nutrition, MFI clients also tend to be more able and willing to support AIDS orphans than non clients.98 In addition to the already stated increase in the use of contraceptives,99 several innovative approaches to deal with disease related crisis have also arisen. This is, of course, necessary not only to counteract the devastating effect the above discussed epidemics can have on MFI clients, but also to not put the viability of the respective institution at risk. One option is to integrate education services in the MFI programs and encourage clients to form solidarity groups.100 It has been shown that these services do not necessarily decrease the effectiveness of the institutions’ employees.101 Another way is to strengthen the ties to NGOs specialize in dealing with the respective disease. Synergies gained through this cooperation can help clients to better deal with the situation and in the end increase their

94

See UNAIDS (2004), p. 23. See CIA (2004). 96 See World Bank (2001), p. 16. 97 See World Bank (2001), p. 12. 98 See Barnes (2001), p. 64. 99 See also Kim et al. (2002), p. 18. 100 See Donahue and Sussman (1999), p. A 14. 101 See Donahue, Kabbucho and Osinde (2001), p. 25. 95

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repayment rates. Various MFIs also have introduced loan and health insurance products or cultivated linkages with organizations doing so.102

It has to be stated that despite the benefits MFIs can provide to people effected by HIV/AIDS or other diseases, microcredit can also be a burden for those who are already chronically ill. These people can still benefit through loans provided to members of the family or the community, or through one of the other services mentioned above.

3.7. Ensure environmental Sustainability

The aims of the seventh MDG are to support and promote the principles of sustainable development and put them into action and to improve the lives of slum dwellers and people without sustainable access to safe drinking water and sanitation. Today climate change is considered as the major environmental problem.103 The Intergovernmental Panel on Climate Change (IPCC) states that human activities contribute to most of the 0.6 °C increase in global average temperature over the 20th century and the predicted increase of another 1.4 to 5.8 °C between 1990 and 2100.104 Emission of carbon from fossil fuel is reported to have the dominant influence on this development. While the total emission was 1.6 billion tons in 1950, it was already 6.5 billion tons in 2000 and is expected to continue to grow.105 This development would lead to a rise in the average sea level of between 9 and 88 centimeters during the next century.106 Furthermore it is predicted that global warming increases the risk of extreme events like cyclones or floods, leads to an increase in the number of people infected by diseases like malaria and decreases the potential crop harvest and water availability, especially in developing countries.107 In 2000 already 1.1 billion people lacked access to clean drinking water and 2.4 billion to basic sanitation.108

102

See Barnes (2001), p. 40; Barnes, Gaile and Kibombo (2001), p. 13 and Grameen Bank (2004). See Gardner (2002), p. 5. 104 See IPCC (2001), pp. 2 et sqq. 105 Worldwatch Institute (2002), p. 52 et sqq. 106 See IPCC (2001), p. 16. 107 See Dunn and Flavin, p. 29. 108 See UNFPA (2004), p. 18. 103

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MFIs contribute to achieving the seventh MDG in several ways. By providing loans for the purchase of renewable energy technologies, they help people in developing countries to increase their per capita energy consumption without increasing the carbon dioxide emission. In a 2003 survey of 140 MFIs from 80 different countries, 21 said they provide credits for solar home systems and another 19 stated they are interested in becoming engaged in this business.109 While Grameen Shakti, a rural energy service company cooperating with the Grameen Bank in Bangladesh, has already installed more than 23,000 solar home systems,110 a 2000 World Bank study reports twelve projects providing solar home systems to rural households with a target of 500,000 installations.111 It has also been suggested that MFIs should raise environmental awareness among their clients,112 but so far no data is available on how staff can cope with this additional task. Many MFIs also provide loans for improvements in sanitation and the water supply of their clients. Varley gives several examples from Africa, Asia and Latin America on how microcredit not only has helped to invest in rainwater collection systems, but also how it has enabled households to increase their water supply through community water associations.113 In addition many MFIs provide credit for the construction of sanitary latrines.114 The situation of slum dwellers is also positively effected by MFIs. Directly through their services in poor urban communities and indirectly by creating jobs in rural areas and therefore easing the pressure on urban labor markets.

3.8. Develop a global Partnership for Development

The eight MDG is about the means to achieve the other seven MDGs. It calls for all players involved in the endeavor of development and poverty eradication to work as partners in a global context.

Again MFIs are contributing to the eight MDG in many ways. This is occurring through international networks that provide funding and technical assistance, through internet information portals and specialized journals, through social and for profit investors and 109

See Wimmer and Barua (2004), p. 174. Barua (2004). 111 See Martinot and Cabraal (2000), p. 6. 112 See Pallen (1997), p. 11; Lal (n.d). 113 See Varley (1995), pp. 39 et sqq. 114 See Jalvaani (1999), pp. 1 et sqq.; Varley (1995), pp. 39 et sqq. 110

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specialized rating agencies. The Bangladesh-based Grameen Trust is one of around 30 networks in operation. Besides collaborating with over 200 MFIs around the world, it has directly operated three projects in Afghanistan, Kosovo and Myanmar.115 While the ACCION network serves almost 1.2 million active clients in 19 countries throughout America and sub-Saharan Africa,116 FINCA’s network operates in three different continents where it serves more than 200,000 customers.117 In November 1997 the MicroBanking Bulletin (MBB) was first published by the microfinance program at the Economic Institute at the University of Colorado.118 Its aim is to develop a database on the financial performance of MFIs from all over the world which is now available through the Mix Market, an internet portal that also provides information about more than 300 MFIs, investors, donor institutions and other related development organizations.119 Two years later the Marriott School at Brigham Young University started to publish the Journal of Microfinance which has an editorial board of microfinance experts from all over the world and offers the latest research on this topic.120 In the late 1990s a number of companies became attracted by the microfinance market and started to invest in MFIs. The German based Internationale Mikro Investitionen Aktiengesellschaft (IMI), for example has already invested more than €46 million in 19 MFIs in four different continents and plans to invest another €27 million before October 2004.121 BlueOrchard Finance, a microfinance investment consultancy, has channeled more than $60 million worth of private capital to some 40 MFIs worldwide through investment funds and bond issues.122 Big commercial banks like Citigroup, Deutsche Bank and ABN Amro also started to get involved in the microfinance sector, although this is mainly still perceived as a public relation activity and not as a for-profit investment.123 There are also several rating agencies specializing in MFIs. Dependent on their rating, these institutions can help MFIs to receive money from donors or investors. Four of the most prominent rating agencies are PlanetFinance

115

See Grameen Trust (n.d.). See ACCION (n.d.). 117 See FINCA (n.d.). 118 See MicroBanking Bulletin (1997), p. 5. 119 See The Mix (2004). 120 See Woodworth and Woller, (1999), p. 6. 121 See IMI (2004). 122 See BlueOrchard (2004b), p. 3. 123 See Busch and Kort (2004), p. 8; Fischer (2003), p. 6. 116

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with headquarters in France, M-Cril based in India, Microfinanza in Italy and MicroRate with offices in the USA and South Africa.124

124

See Microfinance Rating and Assessment Fund (2004).

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4. Microcredit Outreach 4.1. The Demand for Microcredit

Although it has been shown that microcredit is one of the most effective poverty eradication tools, that it is considered a human right by some of its advocates125 and it can contribute considerably to the development of the financial sector in developing countries, an enormous gap still exists between supply and demand for microcredit.

Estimates on the demand for microcredit vary greatly. While USAID estimates the number of potential microfinance clients is between 100 and 200 million,126 the Microcredit Summit Campaign states the total demand is more than 234 million people.127 CGAP recognizes some 500 million micro-entrepreneurs who have the potential to become customers of MFIs.128

Marguerite Robinson calculates the demand as follows: “Assuming five people to a household among the 4.5 billion people living in low- and lower-middle-income economies in 1999 (World Bank, World Development Report 2000/2001), there are 900 million households in those economies. If, estimating conservatively, we assume that informal commercial moneylenders supply credit to 30 percent of these households at least once a year, this would mean that there are 270 million households borrowing from informal moneylenders in a year. Undoubtedly, however, many of these households borrow multiple times within a year.”129 Although this is a very rough estimate, assuming big differences in demand for microcredit in different countries, the number seems to be the most meaningful. In contrast to the estimate of the Microcredit Summit Campaign it does not include every single household living on less than $1 a day, but, according to the definition presented in chapter 1., it counts everyone who has no regular access to traditional financial institutions, but demands such services. Obviously not all people who draw on moneylenders have financial needs that could be matched through MFIs. On the other hand, interest rates charged by moneylenders tend 125

See Yunus (1998b). See Christen et al. (1995), p. 15. 127 See Daley Harris (2003), p. 22. 128 See Robinson (2001), p. 26. 129 See Robinson (2001), p. 215. 126

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to be considerably higher than those of MFIs. The former normally charge effective monthly rates between ten and several hundred percent per month, the latter generally charge between 1.5 and five percent which enables them to attract additional clients.130

4.2. The Supply of Microcredit

In spite of the impressive growth of MFIs around the world, the demand for microcredit is far from being met and this it is not likely to change in the near future. The Microcredit Summit Campaign reported that by the end of 2002, 67,606,080 clients131 were reached through MFIs and that this number has increased by almost 40 percent annually for the last five years.132 It is likely that this tremendous speed of growth is at least partly due to including MFIs which have not been reporting to the Campaign from its start in 1997 although they have already existed at that time. These institutions contributed 22 percent to the growth in poorest clients in 2000, 57.8 percent in 2001 and 33.8 percent in 2002. The growth of the poorest clients increased at least party because of an expanded definition of the term “poorest”.133 Since the contribution of MFIs which previously have not been considered can be expected to decrease dramatically, the growth rate as stated by the Microcredit Summit Campaign can not be used to predict the future development of the microfinance sector. Another factor likely to slow down the growth is the concentration of microfinance activities in some Asian countries where almost 90 percent of the poorest microfinance clients live today.134 Even if one takes into consideration the relative size of Asia’s population and the fact that many MFI customers in Latin America are relatively better off and therefore are not included in this numbers, the coverage of this part of the world far exceeds the others. In Bangladesh for example, the microfinance market is close to saturation. More than eleven million households were served by several hundred MFIs in 2001.135 Among them are Grameen Bank and BRAC, two of the best known MFIs in the world, both providing their services to more than three million customers. With an average household size of five it can be assumed that in Bangladesh the number of people 130

See Gibbons and Meehan (1999), pp. 144 et sqq. 41,594,778 of them belonged to the poorest before they took their first loan. The relative numbers in the following are for these poorest clients only since no other current data was available. It can be assumed though, that this works as a good proxy for the total number of clients. 132 See Daley Harris (2003), p. 3. 133 See Daley Harris (2003), p. 17. 134 See Daley Harris (2003), p. 21. 135 See Ahmed (2003). 131

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benefiting from microcredit already surpasses the number of people living below the poverty line, which is estimated to be slightly higher than 50 million.136 Therefore Bangladesh will not contribute to the growth of the microfinance sector as much as it has done in previous years. This is also true for Vietnam, Thailand and to a lesser extent Indonesia. Eight of the nine MFIs serving more than one million clients are based in these countries while the ninth is based in India where, due to its one billion inhabitants, the microfinance market is still far from saturation.137 In contrast, the supply in Brazil meets less than two percent of the demand, which is estimated to be about 5.8 million potential clients.138 For these reasons, greater amount of future growth must come from smaller or new MFIs in Latin America, India, China and especially Africa, where almost 30 percent of the world’s poorest live.139

4.3. Meeting the Demand

To fill the gap between the demand and supply for microcredit, tremendous investments are necessary to increase the loan funds of MFIs and build up their capacity through upgrading existing institutions, or building new ones. Again, estimates on how much capital is needed vary greatly with the highest being at more than $300 billion.140

In 1997 the Microcredit Summit Campaign estimated that $21.6 billion would be needed to achieve their goal of providing microcredit to 100 million of the poorest households. This was calculated in the following way: Since some 8 million people already received microcredit in 1997 another 92 million needed to be reached. Assuming $200 ($150 for loan fund and $50 for capacity building) would be needed per person for 88 million people in developing countries and $1000 ($500 for loan fund and $500 for capacity building) would be needed for 4 million people in industrialized countries, the capital required ends up to be $21.6 billion.141 For several reasons, this

136

See CIA (2004). These institutions are: The Grameen Bank, BRAC, Proshika and the Association for Social Advancement (ASA) in Bangladesh; the Vietnam Bank for Agriculture and Rural Development (VBARD) and the Vietnam Bank for the Poor (VBA) in Vietnam; BRI in Indonesia; Bank for Agriculture and Agricultural Co-operatives (BAAC) in Thailand; the National Bank for Agriculture and Rural Development (NABARD) in India. See Ahmed (n.d.), p. 1; Llanto (n.d.), p. 337. 138 See Mezzera (2002), p. 23. 139 See See Chen and Ravallion (2004), p. 29. 140 See Clear Profit (2004); Prisma (2002). 141 See The Microcredit Summit (1997). 137

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number is not qualified to predict the total amount of capital needed to meet the demand for microcredit. On the one hand, it focuses mainly on the low end of the market and therefore underestimates the average loan fund needed. On the other hand it does not take the increasing capital demand from established MFIs into account. A survey of 119 MFIs found that the average loan size to increased from $406 in 2002 to $498 in 2003.142 This is mainly due to the fact that MFI clients once they have paid back their loans, usually demand larger ones for the continual development of their businesses, and therefore further increase the demand for capital of established MFIs. The Grameen Bank for example has a ceiling on its loans which increases or decreases depending on the borrowers performance regarding repayments and savings.143 While the maximum amount for the first loan is fixed at approximately $75,144 the largest loan disbursed so far was $17,195.145 Thus, if one estimates that by now 100 million people are borrowing from MFIs,146 another 170 million need to be reached to meet the estimated demand. Assuming an average of $600 ($400 for loan fund and $200 for capacity building) is needed for every new client and another $200 is needed for every existing client to meet the demand for larger loans, a total of $122 billion is required. This rough estimate helps to convey the approximate amount of capital needed to meet the demand for microcredit.

So far the demand for funds is being partly met by donors which annually provide between $500 million and $1 billion,147 and through the mobilization of local savings. Obviously the amount of money provided by donors will not be able to meet the capital demand for microcredit in the short term. The mobilization of savings on the other hand has been extremely successful in some cases, with BRI probably being the most prominent example. After adapting commercial principals in October 1984 BRI was able to raise its deposits from $161,060 to more than $3.2 billion in 2003, which equals almost two times its current loan portfolio.148 Many other MFIs like BDB in Indonesia,

142

Own calculations. Without BRI the numbers would be $343 and $409. See Appendix 1. See Grameen Bank (2003), p. 13. 144 The accurate amount is 5000 Taka. See Interview with Shaw Newaz. 145 Grameen Bank (2004). 146 According to the data provided by the Daley Harris (2003), an annual increase in MFI clients of approximately 25 percent from 31.12.2002 till the 30.9.2004 would be needed to reach 100 million clients by now. This rated is considerably below the average of the previous five years (38 percent). Therefore it can be assumed as realistic, despite the growth hindering factors presented above. 147 See CGAP (2003), p. 8. 148 See Robinson (2002), p. 273; Mix Market (2004). 143

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XacBank in Mongolia, the Equity Building Society in Kenya or the Centenary Rural Development Bank in Uganda also have managed to cover their loan portfolio fully through internally mobilized resources.149 The Grameen Bank is also expected to do so in the first half of 2005.150 In addition to raising much needed capital for on-lending, the provision of saving services can deliver notable social benefits itself.151

Despite these impressive achievements, even the combination of donor money and local savings at their current levels, will not be able to provide the capital needed to continue the rapid growth of the microfinance sector. Especially in their early years, MFIs rely on outside funding. BRI for example needed six years before the amount of locally mobilized savings surpassed the total amount of outstanding loans. During this time expenses and the loan portfolio were covered through a government grant worth $20 million and two World Bank loans of $5 and $97 million respectively.152 It also benefited from a network of more than 3,600 branches which were established throughout the country during the 1970s and early 80s in order to provide government subsidized credit in rural areas.153

The Grameen Bank, also widely known as a best practice MFI, has received about $175 million in direct and indirect subsidies between 1985 and 1996154 and continues to rely on outside funding at below market interest rates. This is made possible either through special loans from the Bangladesh Bank, or through offering bonds guaranteed by the Bangladesh government155 - an option most MFIs do not have. BDB might be one of the very few examples which were able to finance their activities through savings and commercial loans from the start, but its outreach did not reach a large scale, nor did it serve the low end of the market. In 2000, after 30 years of operation, BDB provides credit to only 10,417 clients with an average loan balance of more than $5,000,156 approximately eight times the countries GDP per capita.157

149

See Mix Market (2004). See Grameen Bank (2004) 151 See Robinson (2001), pp. 263 et sqq. 152 See Seibel and Schmidt (2000), p. 4. 153 See Robinson (2002), p. 180. 154 See Morduch (1999b), p. 8. 155 See Morduch (1999ba), p. 25 et sqq. 156 See Robinson (2002), p. 387. 157 See CIA (2004). 150

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Another problem keeping many MFIs from collecting savings is that they do not want to become a regulated financial institution or do not meet the criteria in their respective country and therefore they are simply not allowed to mobilize savings from the public.

In summary, the examples and numbers presented in this chapter support the argument that the amount of capital that can be raised through donors and locally mobilized savings is not enough for continuous rapid growth of the microfinance sector. If this lack of capital is not met through other sources, the microfinance sector will fail to contribute its full potential to the achievement of the MDGs.

4.4. Microfinance and the Capital Markets

Regardless of whether the amount of capital needed for the development of the microfinance sector is $20 or $300 billion, it represents only a tiny fraction of the worldwide capital markets which are estimated at $30 trillion.158 Therefore, if the microfinance industry were able to tap the financial markets it would be able to grow much faster and soon serve the millions of micro-entrepreneurs which currently can not develop theirs businesses because of capital constrains. Another advantage would be that scarce donor money currently, used for the development of the microfinance sector, could be used for other projects that do not have the potential to draw money from commercial sources. This view is shared by Michael Chu, the former CEO of ACCION who made the following statement in 1998: “Microfinance today stands at the threshold of its next major stage, the connection with the capital markets.”159 Many attempts have been made since that time. Successful MFIs like BancoSol in Bolivia further strengthened their ties to the capital markets which, in some cases, already provide the majority of their funding.160 Microfinance investment advisors like BlueOrchard Finance have become middlemen for MFIs and the financial markets. Nevertheless these links are still far from reaching major scale. Currently only approximately 20 percent of MFIs funding comes from commercial sources,161 and the biggest bond issue dedicated to fund MFIs totals only $40 million. In addition these funds mainly end up

158

See Brynjolfsson (2004). Chu (1998), quoted in Robinson (2001), p. 23. 160 See Robinson (2001), p. 69. 161 See Dominicé (2004). 159

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in established institutions in Latin America or Eastern Europe,162 while smaller organizations only benefit indirectly, if at all.

162

See Clear Profit (2004); IMI (2004).

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5. Credit Rationing Until recently the predominant answer to why commercial banks or venture capital companies did not invest in the microfinance sector was that the poor are not bankable. Besides lacking education and therefore not being able to deal with formal financial institutions, they were viewed as bad credit risk who were unable to invest the borrowed money in a way that would allow them to repay the loan and interest at a commercial rate.163 Another reason that kept attention away from the idea of providing financial services to low income people was the belief that informal moneylenders already met this demand. These skeptics felt affirmed through the poor performance of subsidized credit programs as described in chapter 2.2.. Although it is true, that moneylenders serve a huge number of people in the developing world, they more often than not charge usury interest rates of up to 20,000 percent per month.164 While the argument that poor people are not able to participate in the formal financial market can be overcome by adapting the services accordingly,165 the claim of low returns on their investments is not only proved wrong by empirical evidence, but also contradicts economic theory as will be shown in chapter 5.3.

5.1. The imperfect Information Paradigm

A more elaborate explanation for the short supply of financial services to low income people that can be derived from the imperfect information paradigm based predominantly on the works of Akerlof166 and Stiglitz and Weiss.167 After giving a brief summary of their argument an adaptation of their findings to the microfinance market will be presented and subsequently it will be shown how their conclusions can be overcome.

Behind the imperfect information paradigm lies the theory of asymmetric information which simply states that if two parties conduct a transaction, one is likely to have more

163

See Ghatak (1983), p. 21 et sqq., Yunus (1998), p. 102 et sqq.; Robinson (2001), p. 35 et sqq. See Robinson (2001), p. 17. 165 See Seibel (2003b), p. 2; BRAC (2003), pp. 15et sqq.; Yunus (1998), pp. 151 et sqq. 166 See Akerlof (1970), pp. 488 et sqq. 167 See Stiglitz and Weiss (1981) pp. 393 et sqq. 164

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information than the other and is trying to use that for its own advantage.168 Applied to the banking sector this means that a person applying for credit can better judge the likelihood of repaying the loan than the bank. This is because the borrower has better knowledge of the market she wants to invest in, her skills and her willingness to repay. Moreover the bank might not be able to observe if the borrower uses the loan for the agreed purpose. These occurrences can lead to what is known as adverse selection and moral hazard. In this case adverse selection refers to the problem that banks might attract borrowers with a low probability of repayment if they can not distinguish between high and low risk loan applicants. Moral hazard occurs after the provision of the loan when the borrower spends the money on a purpose different to the prescribed purpose and therefore lowers the probability of repayment. This can happen e.g. by investing in a more risky project or through spending the money on consumption. If the bank is not able to find a cost effective way to avoid these problems, it might raise its interest rate in order to compensate for the increased risk.

Figure 1: Expected Bank Return and the Interest Rate

EXPECTED RETURN TO THE BANK

Source: Stiglitz and Weiss (1981), p. 394

I*

INTEREST RATE

Since this might discourage low risk borrowers from applying for credit and encourage borrowers who continue to take loans to invest in riskier projects, the share of high risk 168

See Neus (1998), p. 85 et sqq.

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borrowers could increase and therefore reinforce the problems stated above. Thus increasing the interest rate beyond a certain point actually leads to a decrease in the expected return to the lender. This context is illustrated in figure 1 with I* being the interest rate which maximizes the expected returns to the bank. Obviously the bank would charge the interest rate I* in order to maximize its expected return. However, this price for capital was not determined through supply and demand as economic theory would predict. It is conceivable, that at the interest rate I* the demand for credit exceeds the supply, but even if loan applicants would offer to pay a higher interest rate, the bank would refuse because it associates such a loan with high risk. As a result credit might be rationed.169

5.2. Adaptation to the Microfinance Sector

Through some adaptations this model can also help to explain the lack of funds provided to low income people. In the original model of Stiglitz and Weiss several assumptions have been made that need to be suspended in order to compare the credit markets for low and high income people and give an explanation why the latter is widely neglected by institutional investors.

Evidently the assumptions that the amount borrowed is equal for each loan applicant and that transaction costs170 need not to be taken into account171 are not suitable for this context. While the average loan size of MFI customers is much smaller than that of clients from traditional banks, the transaction costs, as a share of the loan, are considerably higher, because the lender’s costs for conscientious processing a credit applications are only slightly smaller for a $50 loan than for a $100,000 or a $10,000,000 loan. In figure 2, the R(I)large curve represents the return to the bank for large loans with regard to the interest rate I and the R(I)small curve describes the same correlation for small loans. It is shifts downwards to reflect the assertion that the imperfect information 169

See Stiglitz and Weiss (1981), p. 394. In this paper the term „transaction costs“ refers to the operating expense ratio as defined by MicroRate. It “is calculated by dividing all expenses related to the operation of the institution (including all the administrative and salary expenses, depreciation and board fees) by the period average gross portfolio.” See MicroRate and Inter-American Development Bank (IADB) (2003), p. 16. 171 See Stiglitz and Weiss (1981), p. 396. 170

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paradigm has a more severe effect on poor people, than on the better off. They are expected to have less profitable investment opportunities at their disposal,172 and therefore are more likely to invest in riskier projects which increase the probability of default and hence decrease the expected return to the bank. Additionally, one might expect poor people to be more likely than others to spend borrowed money on consumption instead of an income generating activity, particularly if a crisis arises. It seems understandable, that food or urgently needed medicine are given priority over repaying a loan.

The second shift downwards is due to transaction costs. As stated above they are relatively higher for smaller loans. Obviously transaction costs are incurred in the provision of a large loan as well, but since they are much smaller with regard to the loan amount they are not included in the diagram for reasons of clarity.

Figure 2: The extended Model

EXPECTED RETURN TO THE BANK

Source: Own diagram, adapted from Stiglitz and Weiss (1981), p. 394

Large Loans Small Loans R(I)large

Lack of investment opportunities

Transaction costs R(I)small

I*

172

INTEREST RATE

See Khandker (1998), p. 8

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5.3. Overcoming Credit Rationing for the Microfinance Sector

If the conclusions in chapter 5.1. hold, it is rational for a profit maximizing investor to neglect the microfinance sector as long as no surplus funds in the market for large loans exist. This finding can help to explain the small amount of money provided to the microfinance sector from commercial investors. On the other hand it has to be stated that this theory is reflexive as defined by Soros173. It not only explains, but also forms reality, since it has the potential to reinforce the view of potential investors, that microfinance is a less profitable business.174 Therefore demonstrating under which conditions microfinance can be profitable and, in some cases be more profitable than traditional businesses is of crucial importance.

To provide a fairer view of the microfinance sector, a further assumption of the Stiglitz and Weiss model that needs to be suspended is that all investments made by the borrowers have the same expected return.175 As stated above this contradicts economic theory which claims ceteris paribus capital productivity decreases while the amount of fixed capital increases. In combination with a low capital endowment, micro-enterprises are usually faced with an excess supply of unskilled labor, especially in developing countries.176 Hence small enterprises use far less capital per employee than big business and therefore their marginal productivity of capital is much higher.177 This is intuitive if one imagines an economic actor who wants to invest several packages of capital. Obviously she would invest the first package where she expects the highest return, the second package where she expects the second highest return and so on. Each additional investment opportunity would lead to lower, or at best, equal returns.178 While this simplified example has limitations, it demonstrates how the marginal productivity of capital can diminish. Although it might actually increase until a certain point,179 this can be expected to be so low in this context, that it has no influence on the correlation as shown in figure 2.

173

See Soros (1998), pp. 14 et sqq. See Goodwin-Groen (1999), p. 8. 175 See Stiglitz and Weiss (1981), p. 395. 176 See Perkins et al. (2001), pp. 95 et sqq. 177 See Tschach (2000), p. 8. 178 See Rosenberg (2002), p. 11. 179 See Rechard G. Lipsey (1979), pp. 211 et sqq. 174

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Figure 3: Marginal Productivity of Capital

MARGINAL PRODUCTIVITY

Source: Dornbusch and Fischer (1995), p. 406

0

FIXED CAPITAL

Even if good investment opportunities for poor people exist, the question remains if it is possible to distinguish between high and low risk borrowers. The problem of adverse selection is overcome in a cost effective way in many MFIs.180 For the better off poor for example, institutions like BRI base their decision on whether or not to approve a loan by assessing the repayment ability of potential borrowers from their current income flows and not from the uncertain returns generated by the loan.181 Additionally collateral is used in some cases to attract low risk borrowers.182 In contrast to industrialized countries this is usually not done to cover the potential losses of the lender, but to punish the defaulter and therefore increase her willingness to repay.183

Obviously this does not always work for the poorest, since they often neither posses any goods that could work as collateral nor do they have sufficient earnings prior to obtaining a loan for starting an income generating activity. Here the so called grouplending model can help to identify creditworthy borrowers. The most prominent institution using this approach is the Grameen Bank, which only provides credit to

180

See Farrington (2000), p. 20. See Robinson (2001), p. 80; Robinson (2002), p. 238. 182 See Bester (1985), p. 850 et sqq. 183 See Schmidt and Tschach (2001), p. 15; Robinson (2002), p. 244. 181

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members of a group of five. These groups are formed by their members without interference of the Grameen Bank. Although in this particular case no joint liability exists for the members of the group, nobody can receive a new loan if another member of the group fails to repay on time.184 Therefore the group formation work as a screening process. Since the borrowers do not want to risk their ability to obtain further loans, they carefully select with whom to form a group. Usually the members know each other for a long period of time and therefore they can make a meaningful judgment of each others’ ability to repay the loan. Other MFIs even require the members of a group to mutually guarantee each others’ loans.185 While this might further increase borrowers’ efforts to search for trustworthy group members, it might also keep potential borrowers from applying for a loan since she does not want be held responsible for defaulting members. This mechanism enables MFIs to partly pass on the transaction costs related to the selection of low risk borrowers, to the borrowers themselves.186

Many MFIs have also largely overcome the problem of moral hazard by creating incentives to ensure a high repayment rate, even among the poorest. In some cases their customers go hungry in order to pay back their loan even though no legal action would be taken against them if they would refuse to do so.187 One way to achieve this is to build up peer pressure through the formation of groups as described above.188 Another, and probably the most important, is to limit access to future loans to those borrowers who repay on time. The demand for successive loans is extremely high. In a 1996 survey, 98 percent of BRI borrowers planed to apply for another loan189 and in 1993 less than 30 percent of Grameen Bank loans went to first time borrowers.190 Especially the poorest highly value the option to reborrow191 and many MFIs see this as their primary incentive to ensure repayment.192 According to Schmidt and Tschach, “the net present value of future access to credit works out to be roughly half the value of the economic advantage to be gained from the intended non-repayment.”193 This can also

184

See Yunus (1998), p. 135. See Schmidt and Zeitinger (1997), p. 6. 186 See Stiglitz (1990), p.353. 187 Own observation at the Grameen Bank in Bangladesh (2003). 188 See Hoff and Stiglitz (1990), p. 249. 189 See Robinson (2002), p. 251. 190 See Khandker, Khalily and Khan (1995), p. 93. 191 See Gibbons (2000), p. 15; Hashemi and Schuler (1997), p. 43. 192 See Gibbons and Meehan (1999), p. 160. 193 See Schmidt and Tschach (2001), p. 16. 185

37

explain the occurrence of a drop in the repayment rate once a MFI is no longer seen as viable and therefore is not expected to provide loans in the future.194

With the help of the described mechanisms, MFIs around the world are able to keep repayment rates high. A survey of 69 MFIs from 36 different countries, each serving more than 10,000 clients in 2002, found the portfolio at risk (PAR)195 varied between zero and 23.1 percent. While the average was 3.6 percent, the median was found to be 2.29 percent.196 One of the MFIs included in this sample is BRIs who’s 12-month loss ratio has never exceeded five percent since the start of its transformation in 1984, and from 1997 to 1998, the ratio actually fell from 2.2 to 1.94 percent.197 The latter deserves special attention, because the Asian financial crisis reached its peak at that time with Indonesia being hardest hit , as illustrated by a statement from The Economist in July 1998: "Even with the fierce competition from its neighbors, Indonesia would probably walk away with the prize for Asia's most desperate banking system.”198

The findings that MFIs can adapt their services to low income people in order to ensure a high repayment rate, and that poor people do not lack profitable investment opportunities are illustrated in figure 4. From this diagram it can be seen, that under the new assumptions the interest rate at which the bank maximizes its return differs for large (I(l)*) and small loans (I(s)*). According to the assumption that microentrepreneurs can realize a higher return on their investments, the bank can charge them a higher rate of interest without bearing an increased risk. Nevertheless, in this example, the bank would still serve the market for large loans first because of the higher transaction costs for smaller loans.

The view, as described in figure 4, is shared among most traditional banks and investors in industrialized as well as in developing countries. In addition to neglecting the possibility of generating profits by offering microcredit themselves,199 they also hesitate to lend to MFIs because they consider their business too risky. 194

See Schreiner (1997), p. 64. “PAR shows the real risk of the portfolio by comparing outstanding loan balances for loans with at least one late payment to the outstanding loan portfolio.” Gross and de Silva (2002), p. 22. 196 Own calculations. See Appendix 2. 197 See Seibel (2000), p. 11. 198 The Economist (1998), pp. 92 et sqq. 199 Busch and Kort (2004), p. 8 . 195

38

Figure 4: The extended Model Source: Own diagram, adapted from Stiglitz and Weiss (1981), p. 394; Tschach (2000), p. 17.

EXPECTED RETURN TO THE BANK

Large Loans Small Loans R(i)large

Transaction costs

R(i)small

INTEREST RATE

I(l)*

I(s)*

This attitude can be observed by comparing the ratio of equity to liabilities, which is considerably higher for MFIs than for conventional banks.200 When it comes to MFI start-ups and those MFIs serving the low end market in remote areas even experienced microfinance practitioners are skeptical if interest rates that fully recover all costs can be charged.201

Theoretically there are two ways to adjust this model to arrive at a conclusion which is more favorable with regard to the profitability of microcredit. One is to cut the transaction costs and therefore reduce the downward shift of the R(I)small curve, the other is to allow the bank to further raise the interest rate without experiencing a decrease in its return. This means to increase I(s)* and the respective rate of return.

While in 2000 Tschach stated that transaction costs vary between 10 and 20 percent for loans provided through the group lending model and between 15 and 30 percent for

200

See MicroRate and IADB (2003), p. 30. See Volkery (2004), p. 33; Gibbons (2000), p. 15.; Gibbons and Meehan (2002), p. 29; GoodwinGroen (1999), p. 8. 201

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individual loans,202 it was recommended in 1999 that MFIs should target a range between 15 and 25 percent.203 Some best practices on the other hand have already achieved single digit numbers. Currently BDB might be the most efficient MFI in the world with transaction costs at just 4.7 percent in 2001.204 Even though the low costs of BDB have to be acknowledged, its achievement has to be put in perspective. As stated above, with an average loan balance of more than $5,000 it does not serve the low end market. In addition it works in an environment with a high population density which means that it can reach a relatively large group of borrowers per branch. This is a comparative advantage many Asian MFIs enjoy as can be illustrated by comparing Bangladesh and Bolivia. Both countries are at the forefront of the development of microfinance in their respective continent, but while in Bangladesh more than 1,000 people are living per square kilometer, the average for Bolivia is only eight.205 Therefore it is consistent that the most efficient MFI in Latin America, Fondo Financiero Privado para el Fomento a Iniciativas Economicas, has transaction costs of 11.4 percent.206 With an average loan size greater than the annual GDP per Capita, this institution also does not serve the low end market.207

Despite the impressive efficiency gains in the described examples, most MFIs are far from achieving these numbers and many are unlikely to reach them due to the environment within which they work. A survey of 67 MFIs from 31 countries found the average transactions costs in 2002 to be 27.9 percent with a median of 20.6 percent.208 However, even if these institutions were to increase their efficiency to best practice standards, their transaction costs would still be significantly higher than those of traditional banks providing much larger loans. For these banks the respective costs vary in a range between 0.5 and 3 percent.209 According to this argument, charging interest rates considerably higher than conventional banks is the only way for MFIs to generate higher returns and hence successfully compete for capital.

202

See Tschach (2000), pp. 100 et sqq. See Gibbons and Meehan (1999), p. 149. 204 See M-Cril (2001b), p. 4. 205 See CIA (2004). 206 See MicroRate (2004). 207 In this paper, the term “low end” refers to the definition of the MBB (2003), p. 54. Loans worth less than 20 percent of the respective country’s GDP per capita are described that way. 208 Own calculations. See Appendix 3. 209 See M-Cril (2004a), p. 16. 203

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6. Interest Rates 6.1. Setting the right Interest Rate

Before addressing the questions, to what extend MFIs can raise their interest rates without putting the repayment rate or the social benefits of their customers at risk, a basic model will be presented. This model will illustrate how a MFI should set their rates in order to not only cover their costs, but also to generate an appropriate profit to finance further growth and attract additional capital. In 2002 CGAP revised its 1996 paper “Microcredit Interest Rates”,210 which outlines the standard method of setting sustainable interest rates for commercially oriented MFIs. According to this model the annual effective interest rate (I) should be calculated as shown below:

(1.)

I=

TA + LL + CF + K − II 1 − LL

With each variable being a decimal fraction of the average outstanding loan portfolio whereby TA stands for the transaction costs as described above, LL represents the annual loan loss and II stands for investment income, e.g. on liquid assets. The cost of funds (CF) consist of the interest and administrative costs needed to obtain deposits and commercial loans as well as of the imputed costs on equity due to inflation. K stands for the capitalization rate, which represents the net real profit. Keeping this rate high is important to finance the future growth of the loan portfolio. This can be either done through investors attracted by high profitability or through borrowing. For the latter an increase in equity is inevitable since it is required as a safety cushion by lenders.

Since this model ignores the timing of cash flows and does not take taxes into account, it has to be regarded as fairly imprecise and hence should not be used for business planning. Nevertheless, it can be used for an approximation of the interest rate that a MFI would need to charge to provide its services as planned. In the following I will

210

See Rosenberg (2002), pp. 1 et sqq.

41

apply this model on a fictitious MFI working in a rural area of Latin America with low end customers. In this setting it seems realistic to estimate transaction costs of 35 percent and loan loss rate of two percent.211 If funds were borrowed at commercial terms, their costs can be assumed to equal at least 15 percent of the average outstanding loan portfolio.212 Estimating the capitalization rate is more challenging. First the targeted portfolio growth rate is set at 75 percent. This is slightly below the 75.1 percent average annual portfolio growth between 2000 and 2002 found by a survey of 25 different MFIs based in Central and South America.213 If the extend to which the MFI can leverage stays constant, the loan portfolios growth would be limited by the growth rate of the equity. To express the capital needed for this growth as a share of the loan portfolio, it is necessary to estimate the ratio between the equity and the outstanding loan amount. According to the survey mentioned above, the loan portfolios of the 25 Latin American MFIs equal on average 2.15 times their equity.214 With this number, the capital needed to increase the equity by 75 percent would corresponds to approximately 35 percent of outstanding loan amount (0.75/2.15). Finally the investment income has to be estimated. Since liquid assets that are available short term do not generate high returns, say five percent, it does not seem advisable for the MFI to hold more than a quarter of its loan portfolio in this form. Hence the profit generated in this way would equal 1.25 percent of the outstanding loan amount. Using these numbers, the equation to calculate the annual effective interest rate reads as follows:

(2.)

I=

TA + LL + CF + K − II 0.35 + 0.02 + 0.15 + 0.35 − 0.0125 = = 0.875 1 − LL 1 − 0.02

As a result the MFI would need to charge an annual effective interest rate of approximately 87.5 percent on its loans in order to sustainable provide its services and finance its growth without depending on donor money. This rate might decrease once the MFI matures, the growth slows down and cheaper funds become available, but it will stay significantly higher than any rate charged by conventional banks.

211

MicroRate and IADB (2004), pp. 6 and 16. See MicroRate and IADB (2004), p. 6,16 and 28. 213 Own calculation. See Appendix 4. 214 Own calculations. See Appendix 5. 212

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6.2. Can Micro-Entrepreneurs bear these Rates

Although most MFIs charge rates significantly below 87.5 percent, many critics have strenuously argued against any rates exceeding those of traditional banks. Rosanna Barbero of Oxfam labeled an interest rate of 52 percent charged by a Cambodian MFI “ludicrous, evil and disgusting" and stated, that “[n]o business in the world can make a profit at these interest rates.“215 The former finance minister of Bangladesh Shah Kibria even categorized the 20 percent effective rate charged by the Grameen Bank as to high.216 Many practitioners therefore believe in a strong negative correlation between the interest rate and the demand for loans.217 Others have argued more generally that concentrating on profitability and therefore charging high interest rates diverts MFIs from serving the poorest.218

These views are still shared among many MFI managers, donor agencies and especially investors and politicians. While the former are unwilling to charge interest rates for funds they received “cheaply” (through grants or subsidized loans) and are worried about undermining their social goals by charging higher rates of interest,219 the latter are reluctant to invest for reasons stated above or impose interest caps to catch the attention of the public by pretending to protect the poor from usury rates.220

In chapter 5.3. it has been described that micro-entrepreneurs, due to their smaller capital endowment, have even better investment opportunities than big business. The extend to which this is true has not yet been answered. An indication is the high repayment rate observed within MFIs around the globe. If their customers capital yield did not exceed the charged interest rate, how would they be able to repay? According to Gibbons and Meehan customers of MFIs can generally generate returns of more than 100 percent on their invested capital221 and “[r]esearch in India, Kenya, and the Philippines found that the average annual return on investments in microenterprises

215

See Kate and Roeun (2004). See Hodson (2001), min. 11:00. 217 See Morduch (1999a), p. 1594. 218 See Prisma (2002), p. 1. 219 See Schreiner (1997), p. 60; Khandker (1998) p. 106. 220 See Gross and de Silva (2002), p. 32; Jansson and Wenner (1997), p. 36 et sqq.; Campion (2002), p. 59. 221 See Gibbons and Meehan (1999), p. 131. 216

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ranged from 117 to 847 percent.”222. Schmidt and Tschach found the marginal productivity of capital to be as high as 1000 percent for the smallest loans provided by the Bolivian MFI Caja Los Andes.223 These findings are confirmed by the observation of Rosenberg who found, while working as a microfinance consultant for ten years, he never heard of a single MFI having problems to attract clients because interest rates were too high.224

To get a better understanding of the investment opportunities of low income people in developing countries, a typical example on how MFI clients invest their loans is as follows:

Zobair is working as a rickshaw puller in Bangladesh. Like most of his colleagues he does not own the rickshaw he is riding. Therefore he has to pay a daily rent of about $0.30 to the owner. Since his average income of approximately $1.5 per day is hardly enough to feed his family, he is not able to save the $100 necessary to buy a rickshaw on his own. 225 If he could do so through obtaining a loan, his income would increase by $0.30 every day, because he would not have to pay the rent for the rickshaw anymore. Assuming an average of six working days per week, 226 and a life span of the rickshaw of 5 years, his internal rate of return would equal 153.24 percent annually.227 Consequently any interest rate charged by a MFI would be advantageous to him as long as it would not exceed 153.24 percent.228

6.3. Should the Interest Rate be subsidized anyway

Despite the fact that numerous micro-entrepreneurs are able to generate tremendous returns on their investment, obviously not all are able to use capital in such a productive way and therefore can not afford microcredit at commercial rates. In order to reach these people as well, many MFIs continue to use donor money in order to keep their interest rates low. But even if the problems related to subsidized credit as described in 222

See Helms and Reille (2004), p. 3. See Schmidt and Tschach (2001), p. 6. 224 See Rosenberg (2002), p. 10. 225 This examples draws on observations made by the author in Dhaka (Bangladesh) during 2003. 226 Obviously extended holidays are not a viable option in this surrounding. 227 Own calculation. See Appendix 6. 228 This result is only valid if the loan is repaid in daily instalments. For a weekly repayment schedule, the interest rate charged by the MFI must not exceed 151.19 percent in order to still be beneficial for Zobair. 223

44

chapter 2.3. do not arise, it might still not be the most effective tool to help poor people as will be illustrated in the following example:

A MFI faces two groups of potential clients, one with businesses able to generate a return of 125 percent on borrowed capital and another with a possible returns of 50 percent. Assuming budget costs according to equation (2.) the institution needs to charge an interest rate of 87.5 percent and therefore only serves the former group. Supposed each group consists of 50 members demanding $100 loans, the net gains to society (NGS) could be calculated as follows:

(3.)

NGS = 50 × ($125 – $87.5) = $1,875

These gains would be due to the investments of the more profitable group which were made possible through the loans provided by the MFI. The less profitable group would not borrow, since their expected returns are not sufficient to pay for the interest. If the interest rate would be lowered to 30 percent both groups would be able to generate net gains and therefore borrow from the MFI. In order to provide loans at this rate, the institution would need to raise additional capital from non commercial sources. The NGS would be calculated as follows:

(4.)

NGS = 50 × ($125 - $30) + 50 × ($50 - $30) + 100 × ($30 - $87.5) = 0

While the first terms represent the net gains to the respective groups, the last describes the costs incurred in providing the loans. At a rate of 30 percent each loan would need to be subsidized with $57.5. Hence the NGS actually fall after the introduction of the subsidized loan. This is due to the fact that the less profitable group is encouraged to invest in relatively unproductive businesses. In addition, future growth of the MFI could be jeopardized due to scarce donor money which is needed to finance the interest rate subsidy. Therefore it seems more advisable to continue to charge a rate of 87.5 percent and spend the money used for subsidization on other programs, such as health care or education.229

229

In Appendix 7 it will be shown that this result generally holds.

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7. Empirical verification 7.1. Setting up a Sample

To see if the findings of the previous chapters hold, the effects which interest rates charged by MFIs have on their performance will be analyzed in the following. Up to now such a study does not exist mainly because of a lack of adequate data. Most MFIs do not provide accurate information about the annual effective interest rate they charge and therefore make an evaluation difficult.

In 2002, Rosenberg distinguished eight different categories on how microcredit interest rates are quoted.230 These categories differ in several ways. On the contrary to traditional banks for example, it is common for MFIs to state interest rates calculated on a “flat” balance rather than on a declining one. This means that the total loan amount is used to determine the interest rate, ignoring installments paid by the borrowers normally on a weekly or monthly basis. In addition many MFIs charge a certain upfront fee prior to the loan disbursement, require compulsory savings at a lower rate or state their interest rate for semi annual terms only. On the one hand this is done to provide numbers that are more meaningful to MFI customers who often lack the necessary math skills to deal with compound interest, on the other hand it is done to circumvent interest rate ceilings imposed by the government.231 Another problem is that most MFIs offer different loan products without itemizing their respective shares to their total loan portfolio. As a result it is not possible to calculate a weighted average even if the rate stated by the respective MFI could be transformed into an effective interest rate.

Usually the portfolio yield is used as a proxy in order to make statements about the interest rate charged by a certain MFI.232 It is generally “calculated by dividing total cash financial revenue (all income generated by the loan portfolio, but not accrued interest) by the period average gross portfolio.”233 The advantage of this method is that even the hidden costs are considered. Nevertheless it also has a severe shortcoming by not taking late or defaulted payments into account. Therefore the portfolio yield does 230

See Rosenberg (2002), p. 5. MicroRate (2002), p. 8. 232 See MicroRate (n.d.). 233 MicroRate and IADB (2003), p. 39. 231

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not allow to draw conclusions regarding the correlation between the interest rate charged by a MFI and its PAR.

7.2. Examining the Sample

Through the appearance of rating agencies specialized in the microfinance sector more adequate information has become available in recent years. While MicroRate only publishes the portfolio yield of rated MFIs, M-Cril, Microfinanza, Microserve and Planet Rating actually provide information about the annual effective interest rate for at least some of the MFIs they analyze. Although it is labelled differently from all of the four institutions,234 the term describes what is “the highest income or yield that an organisation can earn from its portfolio based on the terms of its loans.”235 Currently236 reports of 39 different MFIs from 20 countries providing accurate information about the effective annual interest rate are available to the public. This sample should be used to verify the findings of the previous chapters.237 Since a comparison is made between the performances of MFIs based in different countries and the ratings have been carried out in different years, several adjustments are necessary. First the inflation of the respective country and the respective year(s)238 has to be taken into account in order to only compare the real rates.239 In addition the average outstanding loan amount per borrower is divided by the respective country’s GDP per capita. Since it can be assumed that “only the poorer households will be willing to take the smallest loans”240, this number can be used as a proxy for the poverty level. Although the sample size is far to small to represent the microfinance sector as a whole, and the results are statistically not significant, the findings can still be used to underline the theoretical conclusions drawn in the previous chapters. In figure 5 the connection

234

M-Cril uses the name “annual percentage rate”. See e.g. M-Cril (2004a), p. 25. Microfinanza uses the name “expected portfolio yield”. See e.g. Microfinanza (2002c), p. 25. Planet Rating uses the name “annual effective interest rate”. See e.g. Planet Rating (2004d), p. 1. Microserve uses the name “theoretical yield”. See Microserve (2003), p. 62. 235 See M-Cril (2004a), p. 25. 236 As of September 28th 2004. 237 See Appendix 8. For exchange rates see FXTOP (2004). 238 Where necessary a weighted average of the annual inflation rate was used. See Appendix 9. 239 This is done through the following formula: ir = (I – F) / (1 + F/100). While ir represents the real annual effective interest rate, F represents the inflation rate. 240 Morduch (1998), p. 1572.

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between the average loan size and the transaction costs as a share of the average loan portfolio is illustrated. Figure 5: Loan Size vs. Interest Rate241 Source: Own diagram.

100.00 90.00 80.00

Interest Rate

70.00 60.00 50.00 40.00 30.00 20.00 10.00 0.00 0

100

200

300

400

500

600

Loan Size

It can be seen that the interest rate is negative correlated to the loans size. This can be explained by the relative higher transaction cost for smaller loans as described in chapter 5.2.. In order to cover their costs, MFIs serving the low end market need to charge higher rates to their customers than those organizations serving the better off poor. Obviously there are several assumptions to this general finding. Especially striking are the two MFIs with an average loan size of approximately five times their countries GDP per capita which still high interest rates. This occurrence can be explained by taking a closer look to the respective organizations. One was established less than two years prior to its rating and therefore still bears a lot of start up costs,242

241

The equation for the regression line reads as follows: Y = -0.0219X + 36.5. The correlation coefficient is 0.14. 242 See Microfinanza (2002b), p. 1.

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the other managed to stay profitable while it almost quadrupled its loan portfolio in the last two years.243 Figure 6: Interest Rate vs. PAR244 Source : Own diagram.

35 30 25

PAR

20 15 10 5 0 0

20

40

60

80

100

120

Interest Rate

In Figure 6 the crucial correlation between the interest rates charged by a MFIs and its PAR245 is examined. According to the given sample, the view of traditional banks and investors that a higher interest rate leads to a higher default rate does not hold. Obviously one has to avoid to draw the opposite conclusion, namely that a high interest rates decreases the PAR. The connection illustrated in figure 6 has to be interpreted as follows. Since high interest rates are related to small loans, the low PAR for MFIs charging high interest rates can actually be explained by the tremendous profitability of small size investments. Despite the high rates their customers are even more likely to repay. 243

See Planet Rating (2003b). The equation for the regression line reads as follows: Y = -0.0768X + 6.8433. The correlation coefficient is 0.235. 245 M-Cril only publishes the PAR for overdues greater than 60 days. Therefore these ratios are used as a proxy for the industry standard of 30 days for some Asian MFIs. 244

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Finally it has to be examined if charging high interest rates actually enables MFI to generate higher returns. Although it can be expected that the combination of a low PAR and a high interest rate leads to a high profitability, it still has to be checked if this is not outdone by soaring transaction costs. Figure 7 illustrates the correlation between the interest rate charged by a MFI and its financial self-sufficiency (FSS). The latter is the “[r]atio of total income to total adjusted expenses for the year. Adjustments are made for subsidised cost of funds (relative to market interest rate), equity (with respect to inflation) and in-kind donations.”246 It can be seen that the regression line actually does indicate a positive correlation between the interest rate and the profitability of a MFI. Figure 7.: Interest Rate vs. FSS247 Source: Own diagram.248

180 160 140

FSS

120 100 80 60 40 20 0 0

20

40

60

80

100

Interest Rate

246

M-Cril (2004a), p. 53. The equation for the regression line reads as follows: Y = 0.2839X + 89.01. The correlation coefficient is 0.174. 248 Since the interest rate the customers of the MFI Foccas have to pay includes fees which are not passed on to the institution, its interest rate in this diagram is set at 72.3 percent instead of the 107.15 percent used for figure 6. See Planet Rating (2003b), p. 1. 247

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This result does not necessarily disprove the findings of Stiglitz and Weiss since it can be argued that MFIs simply do no increase their interest rates to the point where they would maximize their expected returns. But if this would be true, why do MFIs not charge higher rates of interest given the demand described in chapter 4.1 and 4.3? In mature markets competition normally ensures prices not to exceed a certain level, but since many MFIs still operate in a “seller’s market”249 this can not explain the findings as described in figure 7. Therefore it has to be assumed that a combination of the unwillingness of many microfinance practitioners to burden their customers with high interest rates and the plain disbelief of traditional investors that micro-entrepreneurs can bear those rates, is responsible for this occurrence.

249

See MicroRate and IADB (2003), p. 9.

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8. Conclusions It has been shown that MFIs are not only contributing significantly to the development of the financial sector in their respective countries, but also that they play an important role to eradicate poverty by providing much needed capital to low income people which are able to generate tremendous returns on their investments.

The fact that the demand for microcredit is not met is mainly due to the view of most for profit investors and traditional banks that offering financial services to poor people can not be done profitable. Because of this conviction they hardly invest in the microfinance sector. This does not have to be the case according to the findings of this paper. Although faced with relatively high transaction costs MFIs are able to charge interest rates at levels that fully recover their costs. Elizabeth Rhyne’s claim “that the most financially viable programs differed from their less viable peers in their willingness to set interest rates at levels that would fully recover costs”250 is backed by the findings of this paper that high profitability is correlated to high interest rates. Theoretical models based on the findings of Stiglitz and Weiss can not be used as a justification to ration credit for the microfinance sector since the interest rate that maximizes profits to the lender can be assumed to exceed 100 percent for many MFIs. Nevertheless this rate is hardly charged, although it has been shown that it can be done. The Mexican MFI Compartamos for example manages to keep its PAR as low as one percent while charging an effective interest rate of more than 100 percent to approximately 160,000 clients.251 In combination with this case, the conclusions drawn from examining the sample of MFIs in chapter 7., the claim of Hulme and Mosley as stated in the introduction can be considered as wrong. MFIs are in fact able to finance rapid growth, stay profitable by charging corresponding interest rates and still have a significantly positive impact on the poverty level of their clients. In the long run gains in efficiency, economies of scale, a slowdown in growth and competition will ensure interest rates drop as it has already been the case in countries like Bangladesh and parts of Bolivia.252

250

See Rhyne (1998), p. 7. See MicroRate (2004). 252 See Microrate and IADB (2003), p. 9. 251

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According to a MicroRate survey many MFIs are more profitable than Citibank and also generate a higher return on investment than commercial banks in their respective countries.253 Investments companies like IMI or Blue Orchard will soon be able to present a meaningful track record of their microfinance investments to potential investors. Ultimately the capital markets can not ignore the facts proving that it actually can be profitable to invest in the microfinance sector, but in order to raise money quickly microfinance practitioners will have to continue to intensively promote their cause not only to for profit investors, but also to donors and multilateral agencies like the World Bank.

253

See MicroRate (2002), p. 10.

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Appendix

Appendix 1: Average loan balance Source: Data is available through the Mix Market (2004).

Organization 2CM ACF ACLEDA ACME ADIM AFK AgroInvest Al Amana AMSSF/MC AREGAK AVFS BancoSol BESA BPR AK BPR BMMS BRI BZMF CBDIBA CCA CCCP CEP CERUDEB CMEDFI CMM - Bogota CMMB COAC Maquita Cushunchic CODES CODESARROLLO Constanta COOPEC CAMEC MN Crear - Tacna CREDIT CRYSTAL FUND DBACD ECLOF - ECU ECLOF - MAL EKI EMT ESED

Loan Portfolio 2002 (US$) 1263602 955503 26846703 1460881 369065 933306 4684249 15593277 612167 3285273 214331 83629049 10563980 613362 133949 1344006170 1592508 216587 2146235 447534 3795400 25093643 227674 4836000 34880 870084

Loan Number of Number of Portfolio borrowers Borrowers 2003 (US$) 2002 2003 1400438 3622 3708 1611401 285 370 39391301 82598 98905 1714844 4283 4600 407798 1122 1395 1706577 250 467 9697411 5208 9629 28677666 78114 101610 1040756 6183 6886 3987011 11841 14377 269819 1883 2866 91175000 42290 56707 15114430 4488 5061 876130 1730 3879 237946 1755 1779 1720072773 3056103 3100358 1922097 1250 1331 242465 5764 2877 4582302 4070 6585 851678 6265 9450 5261957 32291 42132 34873228 34490 44796 250244 3467 3781 8135968 15635 22038 56964 42 190 1855718 6603 7732

280843 6714007 2943822 7152

263091 12935951 3536047 29821

60 4391 16134 92

60 6896 18588 77

3891116 642094 366456 3473701 1154292 441734 11833244 4481589 8579460

4636094 81838 421403 3876526 728312 407227 19245751 5765281 6834742

3671 7532 1379 12812 860 1118 8999 84781 22790

4948 8097 1061 19606 416 1500 13323 92173 20035

54

Eshet Faulu - KEN FDL FIE FINADEV Finance Salone Finca - TAN Finca - UGA FINCORP FJN FMFB FMM Bucaramanga FODEM Fundaci ESPOIR FUNDESER Gasha GGLS Save the Children GK HKL HOPE IAMD IDECE IDF IMCEC - Dakar IMCEC - Thies ISSIA JMCC KAFC KAMURJ Kashf KMBI KPSCA K-Rep KSCS KVT LAPO MEC ADEFAP MEC Bosangani MEDF Meklit Metemamen MFSC MIKRA MIKROFIN Miselini MRFC Mushuc Runa Nirdhan OCSSC

185741 9027332 12503027 35818903 5512586 211312 1293430 2451703 4761288 5473563 308158 6355518

462569 7206344 16545332 41597315 9770323 434298 1854834 2798869 10236896 8037662 1188896 10227000

3308 17463 21306 26468 12775 2225 20648 35610 941 8107 713 25814

6540 15000 25106 33100 14709 7159 27499 36912 858 11133 3558 42464

739181 898561

939824 1883446

1909 5911

2501 9464

798907 323748 64235

1373113 413742 137957

4072 5504 1291

5065 6423 2362

56986 1363365 402799 42132 21312 2031944 254759 199799 221615 1962355 21710638 1003935 2355965 721409 109299 14490447 18978 44075 922478 27788 14357 104113 233170 22123 109261 2951028 9560502 737387 11475440 2089076 2886777 5245186

166971 1420357 603488 56550 39453 2626382 688449 571122 246792 2609723 29241051 1411258 6271498 1626666 128063 20699963 43740 70621 1194633 69248 31086 136257 252232 57778 171090 5008784 18380263 1026639 9133130 5418325 3016171 7658413

951 6648 1098 2780 150 31003 1018 1164 1409 1471 24850 5559 29655 11973 362 38739 140 241 18740 198 364 1278 2084 943 511 4145 5633 10182 163000 3139 29589 42157

2718 5372 1562 2772 232 36580 2983 1261 1698 2333 32097 5691 59389 27266 794 45379 297 373 19139 202 540 1184 3577 1501 687 6095 7426 11431 180000 6232 27457 6215o

55

Otiv Diana Otiv Sambava Otiv Tana Partner PCA PEACE PEDF Piyeli PRESTANIC PRIDE - MAL PRIZMA ProMujer Nicaragua PTF RBST REMECU RUSCA SCSCS SFPI Sidama SPBD Sunlink Sunrise TEBA TIAVO TPC TSPI UNICECAM Wasasa Wisdom XacBank Zakoura

95992 188094 66465 9976448 6878257 420746 164399 809447 2827502 991888 3881581 1006871

638422 437974 673175 15321304 6885926 627295 254233 1206656 3540367 854631 6838978 1424437

229 2002 337 7139 63113 4192 2357 8674 1477 5376 8112 10436

1073 1442 1269 11935 58147 5428 3340 4745 2605 5601 10968 13047

891566 818594 1381705 60887 89423 709497 934260 117807 819138 5412366 108032426 281978 1140429 4650989 3442843 230657 1306066 4910098 15907288

1068881 1058115 1483471 100820 157472 903556 1011798 186029 1056823 8782868 176300111 838905 2267148 5383755 5943804 274908 1404463 9829503 12444768

8500 619 10330 454 873 7728 10267 647 3401 4560 163021 2049 22869 49649 16039 2850 10524 11063 103720

9070 723 13996 994 1007 9552 11346 1740 3833 7256 157776 459 31668 75617 19031 3728 12054 18610 118980

Appendix 2: Portfolio at Risk Source: Data is available through the Mix Market (2004).

Organization ABA ACEP ACLEDA ACODEP ACSI Al Amana Apoyo Integral AREGAK Banco Solidario BancoSol BASIX

PAR 2002 2.02% 4.64% 1.75% 4.52% 2.09% 0.10% 4.83% 6.92% 5.00% 6.60% 12.96%

56

BRI BURO Tangail CEP CERUDEB CMAC Sullana CMM Bogota Compartamos Constanta Credi Fe CREDIAMIGO DBACD EBS EMT ESED FADES Faulu - KEN Faulu - UGA FDL FECECAM FED FIE FINADEV Finamerica Finca - TAN Finca - UGA FMM Bucaramanga FMM Popayan FONDEP FORA Genesis Empresarial IDF KAFC Kashf KMBI K-Rep MiBanco MRFC Nirdhan PADME PAMECAS PCA PRIDE - TAN ProMujer Nicaragua RCPB REMECU SHARE

4.37% 3.50% 2.00% 1.03% 5.67% 1.76% 1.11% 3.04% 2.12% 4.09% 0.10% 8.47% 0.10% 13.11% 23.11% 2.46% 0.96% 2.12% 4.28% 4.36% 5.98% 0.21% 11.33% 0.00% 2.97% 0.94% 0.88% 0.13% 0.64% 11.97% 0.17% 1.06% 0.00% 1.28% 2.29% 3.09% 17.42% 3.73% 0.79% 2.84% 3.79% 0.18% 0.73% 6.00% 1.57% 0.00%

57

Soluci Spandana TEBA TSPI UMU UNICECAM Urwego UWFT WAGES WWB Medellin XacBank Zakoura

1.82% 0.14% 1.06% 3.55% 1.25% 4.05% 2.90% 3.63% 7.36% 2.35% 0.61% 0.95%

Appendix 3: Transaction Costs Source: Data is available through the Mix Market (2004).

Transaction Organization Costs Acci Rural 11.54% ACEP 8.58% ACLEDA 24.30% ACODEP 29.20% ACSI 9.24% Al Amana 25.76% AREGAK 28.56% AssEF 19.19% Banco 18.26% Solidario BancoSol 12.10% BASIX 13.17% BRI 13.53% BURO 20.53% Tangail CEP 17.59% CERUDEB 51.09% CMAC 17.84% Sullana CMM 20.89% Bogota Compartamos 33.41% Constanta 45.97% Credi Fe 19.78% CREDIAMIGO 25.25% DBACD 17.42% EBS 23.77% EMT 32.20% ESED 13.43% FADES 16.73% FAMA 24.36%

58

Faulu - KEN Faulu - UGA FDL FECECAM FED FIE Finamerica Finca - TAN Finca - UGA FMM Bucaramanga FMM Popayan FONDEP FORA Genesis Empresarial IDF

22.53% 49.62% 17.26% 25.87% 40.83% 11.25% 15.63% 107.53% 90.96% 19.25%

K-Rep LAPO MiBanco Nirdhan NWTF OCSSC PADME PAMECAS PCA PRIDE - TAN ProMujer Nicaragua RCPB SHARE Sidama Soluci Spandana TEBA TSPI UMU UNICECAM Urwego Wisdom WWB Medellin XacBank Zakoura

19.09% 35.11% 24.56% 13.25% 31.09% 11.55% 11.31% 17.05% 16.70% 42.73% 50.78%

11.12% 54.77% 34.81% 16.26% 16.95%

20.59% 23.08% 26.62% 29.06% 6.41% 12.13% 38.98% 40.23% 101.20% 74.37% 19.06% 17.03% 41.44% 18.81%

59

Appendix 4: Portfolio Growth Source: Data is available through the Mix Market (2004).

Total Loan Portfolio in 2000 5,889,394 4,327 1,514,681 11,100,088 39,632,165 78,031,172 14,596,365

Total Loan Portfolio in 2002 7,258,487 88,594 2,591,240 12,018,280 103,420,000 83,629,049 28,055,720

348,443

870,084

CODESARROLLO 1,374,801 Compartamos 10,804,279 Crear - Tacna 3,778,816

6,714,007 41,825,248 3,891,116

Credi Fe ECLOF - ECU FADES FAMA FIE Finamerica FINCOMUN FMM - Popayan

781,616 179,247 11,948,814 6,201,411 22,524,645 16,575,104 3,278,938 6,241,821

9,393,936 1,154,292 15,501,578 8,835,134 35,818,903 16,097,113 5,696,556 12,212,867

FONDECO Genesis Empresarial MiBanco Mushuc Runa ProMujer Nicaragua WWB - Medellin

3,456,625 11,154,234

5,101,801 17,746,288

36,964,072 90,737 426,541

95,873,333 2,089,076 1,006,871

2,891,207

4,580,998

Organization ACODEP Adelante ADRI AgroCapital Banco Solidario BancoSol CMAC - Sullana COAC Maquita Cushunchic

Appendix 5: Portfolio vs. Equity Source: Data is available through the Mix Market (2004).

Organization ACODEP Adelante ADRI AgroCapital Banco Solidario BancoSol CMAC - Sullana

Loan Portfolio Equity 7,258,487 1,921,447 88,594 72,973 2,591,240 781,477 12,018,280 10,766,101 103,420,000 12,961,000 83,629,049 15,299,415 28,055,720 6,210,291

60

COAC Maquita Cushunchic CODESARROLLO Compartamos Crear - Tacna Credi Fe ECLOF - ECU FADES FAMA FIE Finamerica FINCOMUN FMM - Popayan FONDECO Genesis Empresarial MiBanco Mushuc Runa ProMujer Nicaragua WWB - Medellin

870,084

260,331

6,714,007

1,285,103

41,825,248 3,891,116 9,393,936 1,154,292 15,501,578 8,835,134 35,818,903 16,097,113 5,696,556 12,212,867 5,101,801 17,746,288

18,350,446 1,016,912 863,848 459,226 4,138,972 6,377,663 5,360,484 3,817,960 1,042,241 8,510,494 1,658,075 4,765,816

95,873,333 2,089,076 1,006,871

24,261,889 434,311 1,196,015

4,580,998

2,213,249

Appendix 6: Internal Rate of Return Source: Excel (German Version) calculation based on own data.

A

1 2 3 4 5 6 7 8 9 10 ... … … 1562 1563 1564 1565 1566 1567 1568

Date

B

C

D

Interest Rate / Internal Rate of Cash Flow Return Balance

01.01.2004 02.01.2004 03.01.2004 04.01.2004 05.01.2004 06.01.2004 08.01.2004 09.01.2004 … … … … … … 23.12.2008 25.12.2008 26.12.2008 27.12.2008 28.12.2008 29.12.2008 30.12.2008

-100 0.3 0.3 0.3 0.3 0.3 0.3 0.3

153.24%

… … … 0.3 0.3 0.3 0.3 0.3 0.3 0.3

61

100 99.9548955 99.909676 99.8643412 99.8188909 99.7733247 99.9826083 99.9374595 … … … 1.77951388 1.48859724 1.19239161 0.89543096 0.59771337 0.29923692 -3.434E-07

E

F

Interest

Principal

0.25489547 0.25489547 0.2547805 0.25466524 0.25454968 0.25443383 0.50928361 0.25485114 … … … 0.00528711 0.00908336 0.00379437 0.00303935 0.00228241 0.00152354 0.00076274

0.04510453 0.04510453 0.0452195 0.04533476 0.04545032 0.04556617 -0.2092836 0.04514886 … … … 0.29471289 0.29091664 0.29620563 0.29696065 0.29771759 0.29847646 0.29923726

The formula in the square C3 reads as follows: “XINTZINSFUSS(B3:B1568;A3:A1568;1.5)”. Appendix 7: Subsidized Interest Rates and the Net Gains to Society Source:Own calculations

For simplicity only two groups of potential clients are distinguished. This is possible since using a continuum of potential clients would lead to the same results. In addition it is assumed that the amount of every loan (L) is equal and that all clients invest the whole loan amount. Group H consist of NH potential clients with a rate of return (rH) on their invested capital higher than the cost recovering interest rate charged by the MFI (iC). Group L consists of NL potential clients with a rate of return (rL) on their invested capital lower than iC. If the MFI charges iC, the NGS can be calculated as follows: NGSC = NH × (rH - iC) × L Since rH is bigger than iH (rH > iC) according to the definition presented above, the term NGSH must be positive as long as NH does not equal zero. If the MFI introduces a subsidised interest rate (iS) lower than rL, the NGS can be calculated as follows: (1.)

NGSS = NH × (rH - iS) × L + NL× (rL - iS) × L + (NH + NL) × (iS - iC) × L

(2.)

NGSS = NH × (rH- iC) × L + NL× (rL - iC) × L

The term (NH + NL) × (iS - iC) × L describes the amount of money that is needed in order to subsidize the interest rate to the level iS. By setting up an inequation, it can be examined under which conditions the NGSS are bigger than the NGSC: (3.)

NGSS > NGSC

(4.)

NH × (rH- iC) × L + NL× (rL - iC) × L > NH × (rH - iC) × L

(5.)

NL× (rL - iC) × L > 0

(6.)

r L > iC

62

So the NGSS could only be bigger than the NGSC, if equation 6. holds. This is impossible since it violated the definitions presented above. Therefore the introduction of subsidized interest rates is always accompanied with a drop in the NGS.

Appendix 8: Interest Rate vs. Loan Size vs. FSS vs. PAR. Source: Data is available through the Mix Market (2004).

Organization BTFF Besa Foundation RFF PSHM Mi-Bospo Prizma Agroinvest KCLF Foccas ACEP Padme Papme Vital Finance Finadev ABA ASBA DBACD SBACD AMSSF Fondep Zakoura ENDA BSFL Share Spandana SWC SKS Grama Vidiya Sneha Buro Upap Seeds EMT TPC

Country Kyrgyz Republic Albania Albania Albania Bosnia Bosnia Serbia Kazakhstan Uganda Senegal Benin Benin Benin Benin Egypt Egypt Egypt Egypt Morocco Morocco Morocco Tunisia India India India India India India India Bangladesh Pakistan Sri Lanka Cambodia Cambodia

Loan Size (divided by the GDP per Capita)

Real Interest Rate

FSS

PAR

38.03

482.60

148.7

6.98

21.84 11.98 21.94 29.74 34.74 22.01 57.13 107.16 (72.3) 32.80 27.15 30.86 26.76 17.38 25.79 25.97 24.90 25.00 69.26 51.99 44.14 40.08 19.54 34.26 29.42 18.64 22.48 31.76 34.51 32.06 20.73 10.04 43.24 44.70

160.76 57.83 0.00 54.99 35.69 44.01 17.13

68.4 47.64 57.58 126.1 103 87.5 86.5

2.60 2.77 1.98 0.20 0.64 0.20 0.90

15.50 209.41 135.75 536.59 93.94 148.50 16.13 17.02 16.43 17.34 7.87 6.50 9.00 13.51 32.69 14.40 16.18 26.26 19.12 12.21 14.33 18.82 81.83 10.88 16.76 20.35

58.4 164.3 166.7 140.5 101.5 102.4 116.8 125.2 107.3 98.2 130.6 88.9 98 110 85.6 95.1 136.7 42 57.8 76 93.3 109.3 75.7 60.5 101.6 83.2

2.9 1 0.8 2.4 1.7 2.1 8.8 6.8 0 6.7 0.4 0.71 0.06 1.02 9.9 0 0.26 30.36 0 1.8 0 1.7 0.3 28.7 0.1 2.7

63

WTF TSKI Cacja ACME Grupo Cama

Phillippines Phillippines Ecuador Haiti Mexico

58.16 57.94 8.62 60.43 89.61

64

10.95 7.77 61.89 69.34 2.67

94.1 96 94.60 116.85 107.17

7.4 7.9 8.97 4.50 2.88

Appendix 9: Inflation vs. GDP per Capita Source: IMF (2004).

Country

Subject Description

Units

Albania

Gross domestic product per capita, current prices US dollars Units 1083.988 1238.206 1396.172 1757.778 2229.712

Albania

Inflation, annual percent change

Bangladesh

Gross domestic product per capita, current prices US dollars Units 329.310 327.980 344.494 369.495 393.717

Bangladesh

Inflation, annual percent change

Benin

Gross domestic product per capita, current prices US dollars Units 361.340 368.213 405.951 494.725 560.129

Benin

Inflation, annual percent change

Percent Percent Percent

Scale

2000 0.0 2.2 4.2

2001 3.1 1.5 4.0

2002 5.2 3.8 2.4

2003 2.4 5.4 1.5

2004 3.4 6.4 2.6

Bosnia and Herzegovina Gross domestic product per capita, current prices US dollars Units 1255.837 1321.429 1466.090 1807.392 2070.508 Bosnia and Herzegovina Inflation, annual percent change

Percent

5.0

3.2

0.3

0.2

0.9

Cambodia

Gross domestic product per capita, current prices US dollars Units 276.413 280.352 296.577 305.940 321.247

Cambodia

Inflation, annual percent change

Ecuador

Gross domestic product per capita, current prices US dollars Units 1227.711 1590.267 1804.362 1957.183 2082.156

Ecuador

Inflation, annual percent change

Egypt

Gross domestic product per capita, current prices US dollars Units 1550.982 1461.431 1278.462 1188.060 1083.158

Egypt

Inflation, annual percent change

India

Gross domestic product per capita, current prices US dollars Units 454.206 458.849 472.915 542.554 602.625

India

Inflation, annual percent change

Kazakhstan

Gross domestic product per capita, current prices US dollars Units 1236.159 1490.946 1655.148 2000.598 2579.554

Kazakhstan

Inflation, annual percent change

Kyrgyz Republic

Gross domestic product per capita, current prices US dollars Units 278.256 306.130 321.079 349.632 380.637

Kyrgyz Republic

Inflation, annual percent change

Percent Percent Percent Percent Percent Percent 65

-0.8 -7.7 2.8 4.0 13.4 18.7

0.2 37.7 2.4 3.8 8.3 6.9

3.3 12.6 2.4 4.3 5.9 2.1

1.2 7.9 3.2 3.8 6.4 3.1

2.0 3.2 5.2 4.7 6.8 4.5

Mexico

Gross domestic product per capita, current prices US dollars Units 5957.486 6274.468 6425.203 6111.754 6377.064

Mexico

Inflation, annual percent change

Morocco

Gross domestic product per capita, current prices US dollars Units 1161.302 1162.154 1199.584 1432.725 1541.041

Morocco

Inflation, annual percent change

Pakistan

Gross domestic product per capita, current prices US dollars Units 437.381 400.356 440.471 492.913 538.120

Pakistan

Inflation, annual percent change

Philippines

Gross domestic product per capita, current prices US dollars Units 979.423 900.225 950.707 963.765 1018.710

Philippines

Inflation, annual percent change

Percent

4.3

6.1

3.1

3.0

5.4

Russia

Inflation, annual percent change

Percent

20.8

21.5

15.8

13.7

10.3

Senegal

Gross domestic product per capita, current prices US dollars Units 478.704 474.460 507.408 634.171 714.622

Senegal

Inflation, annual percent change

Percent Percent Percent

Percent

9.5 1.9 4.4

0.9

6.4 0.6 3.1

3.0

5.0 2.8 3.2

2.3

4.5 1.2 2.9

-0.0

4.4 2.0 4.6

0.8

Serbia and Montenegro Gross domestic product per capita, current prices US dollars Units 1031.263 1389.384 1882.036 2491.799 2795.805 Serbia and Montenegro Inflation, annual percent change

Percent

69.9

91.1

21.2

11.3

7.9

Sri Lanka

Gross domestic product per capita, current prices US dollars Units 846.811 796.025 828.256 904.330 982.466

Sri Lanka

Inflation, annual percent change

Tunisia

Gross domestic product per capita, current prices US dollars Units 2034.360 2065.140 2149.421 2534.823 2881.273

Tunisia

Inflation, annual percent change

Uganda

Gross domestic product per capita, current prices US dollars Units 293.404 249.682 279.587 280.802 287.204

Uganda

Inflation, annual percent change

Percent Percent Percent

66

6.2 3.0 4.5

14.2 1.9 -2.0

9.6 2.8 5.7

6.3 2.8 5.1

6.4 3.4 3.5

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