Title: Why Does the Microcredit Borrow Rate Differ Across Countries? A Cross-Country Study

Authors:

- Sofia Pereira

(Main contact)

Businesses Manager at Banco BilbaoVizcaya and Argentaria (Office at Viana do Castelo); Student at the Master Course of Social Economics (University of Minho) Contact: Sofia.pereira@kanguru.pt

- Paulo Mourao

Department of Economics (School of Economics and Management); University of Minho, Braga – Portugal; Contact: paulom@eeg.uminho.pt

Abstract:The purpose of this paper is to study the socio-economic variables that influence the number of micro-credit projects worldwide. We also intend to study the socio-economic variables that lead to a higher default rate. In order to do this study, we will use a database from MIX and include some more variables. We intend to explore why the number of debtors/lenders is higher or lower depending on each country, and what variables influence this behavior. This will allow us to distinguish the regions where there is more microcredit and on what basis and why, in some cases, it is found to have a higher incidence of default. Our results showed that green cases (characterized by a lower probability of default) are increased when more collateral value is required and the case is not in Africa. Higher levels of population under the poverty line, higher levels of the Gini index, and being an African country lead to higher levels of yellow cases. We observed that the percentage of red cases (characterized by a higher probability of default) tends to rise if we have a smaller value of firms using banks to finance investments, if we observe a reduced expression of small firms, and if we have smaller values of collateral needed for a loan.

Key-Words: Microfinance, determinants of borrow rates, default rates, cross-country comparison

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1. Introduction

Microcredit is now practiced around the world. It has become a global

industry where we can see the participation of many actors such as non-

governmental organizations, governments, private and state banks and other aid

organizations. It has become a powerful tool to fight against poverty.

Now, it's time to reevaluate microcredit’s path, check where it is heading,

and evaluate the main challenges it will face, so we never lose sight of the main

objective for which it was created: the alleviation of poverty. It is necessary to

assess the main reasons leading to its failure in order to overcome default. This

paper is divided into sections. Section two is divided into two points, where we

do a literature review focused on the practice of microcredit around the world

and where we also discuss causes of failure cited by some authors. In section

three, we present our data, our model and our empirical results on microcredit

default. Finally, in section four we present our conclusions.

2. A WorldWide Picture – Microcredit around the world and causes for

microcredit default around the world

2.1 Microcredit around the world

Microfinance (specifically microcredit) has been practiced probably since the first

human being, but until the 1980's, it was seen as a more informal or charitable 2

perspective (Ming-Yee, 2007).

Muhammad Yunnus founded the Grameen Bank (Village Bank) in 1983. This was

the first institution worldwide to officially practice microcredit. At Grameen Bank’s

inception, the purpose was to provide loans without requiring collateral to people in

need and those excluded from traditional banking systems. This would allow them to

escape from extreme poverty. Yunnus said, "Poverty is a chronic disease. It cannot be

cured with ad hoc measures. There may be short-term measures, but we must keep in

mind a long-term strategy when there is a strategic move fast" (Yunus, 1997). It also

summarizes an important point--that microcredit should be seen as an instrument to

combat poverty and not as the only solution. When asked about the origin of Grameen's

innovative ideas and how he managed to create a bank when he was not a banker and

had no banking experience, M.Yunnus replied, “We look at conventional banks and do

everything in reverse” (Yunus, 1997). Internationally, the Grameen model has been

replicated by several countries, both in developed and in developing countries: Africa,

Asia, Oceania, the Americas and Europe (Armendáriz and Morduch 2010). Nowadays,

microcredit is developed in many countries around the world: there are more than

10,000 microfinance institutions. It represents a business of €50 billion each year for

500 millions of borrowers (Augé, Lebrun and Piozin, 2010).

Various well-succeeded schemes around the world make us believe that the

success of microcredit in developing countries has proven that it is not the lack of will

that explains why the poor remain poor; it is the lack of opportunity (McDowell, 2006).

Poverty is not evenly distributed around the world and the gap between the richest and

poorest is still large.

Figure 1 – Population living under poverty lines

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In 2000, world leaders at the United Nations committed themselves to the

ambitious goal of reducing poverty by 50% by 2015. The Grameen Bank was the

beginning of many well-succeeded stories around the world. Grammen-type loans have

in common the fact that they target communities that have difficult access to finance

and financial services, always with the idea that funding the loans are not charity.

“Financial Access 2010” was a report made by CGAP (Consultative Group to Assist the

Poor) and the World Bank Group. In this report, the authors developed an analysis of

142 countries regarding information concerning the changes in access to financial

services (some authors estimate that about half the families in the world lack access to a

bank account). They came up with some fundamental and determining conclusions: in

countries with low income, where financial access remains limited as a rule,

microfinance institutions have more responsibility for promoting activities related to

financial inclusion, such as savings and access to rural areas. The microfinance industry

has grown sustainably and with a high level of professionalism in the world. In this

report, it is mentioned that overall high-income countries have on average the most

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comprehensive consumer-protection framework: “60% of these economies have an

allocated team working on consumer protection issues.” Regulation was an important

subject to analyze while talking about microcredit. Although the number of loan

accounts stayed almost unchanged worldwide, lending volumes declined (number of

outstanding loans decreased 57% of the economies in this report, with a few

exceptions). There is evidence that access to financial services is improving and that

macroeconomic stability is fundamental for access to credit services. Low-income

countries had better growth in the number of bank branches, Automated Teller

Machines (ATM) and Point-of-Sale terminals (POS). This also tells us that access to

finance and financial services is improving in low-income countries. According to

Financial Access 2010, commercial banks have two thirds (66%) of all bank branches,

followed by cooperatives (23%) and microfinance institutions and specialized financial

institutions (11% combined).

Practices of micro-credit should be studied, taking into account demographics

and the differences between regions. CGAP does a territorial division as follows:

1. East Asia and Pacific (EAP)

2. Europe and Central Asia (ECA)

3. Latin America and Caribbean (LAC)

4. Middle East and North Africa (MENA)

5. South Asia (SA)

There is a higher percentage of microcredit and a higher number of MFIs (MicroFinance

Institutions) in the Asian and Pacific countries (Latifee, 2006). This region also has a

major global "slice" of the world population (22%) with a 25% concentration of the

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world's poor (according to figures from CGAP). In these countries, there has been

relative resistance to the world international financial crisis. On average, those are

countries where financial activity occurs with great state control.

With regards to Europe and Central Asia, this region comprises 22 countries,

with a total population of about 366 million people. In January 2009, there were about

8,200 active microfinance institutions. In LAC (Latin America and Caribbean), there is a

regulatory and financial sector growing rapidly in all countries. There has been an

increase in regulation and direct intervention of governments. As for MENA countries

(Middle East and North Africa), there have been many recent developments in the

region, particularly with regards to increased violence. In this report (Financial Access

2010), authors state that microfinance institutions operating in the MENA region are

more dependent on debt and overall financing of the remaining regions. In South Asia,

there is a wide regional disparity in microfinance distribution: countries with decades of

experience (e.g., Bangladesh) and countries now taking the first microfinance steps (e.g.,

Afghanistan) according to Financial Access 2010.

India is the fastest growing country in the world of MFIs. Recent financial crisis

has not reached the South Asia region yet, although some problems have emerged in

Pakistan reimbursement, and more recently some signs of trouble have appeared in

India and Bangladesh. Pakistan and Nepal set up a class for microfinance banks in

Bangladesh and recently created a new authority to regulate microcredit. India, Sri

Lanka and Bhutan are working to analyze the regulatory framework of microcredit

schemes. Interest rates continue to be a very sensitive topic.

Some believe that MFIs were created fundamentally with a common goal--

poverty reduction--but many of them are starting to look like traditional for-profit

financial institutions. Some critics say that MFIs are not commercial enough. Counts

(2008) proposes a new model “that could make microfinance both more relevant to the 6

world’s poor and more profitable in the long term.” Counts (2008) believes that it is

necessary to re-imagine microfinance MFIs in order to regain the most skeptical public’s

trust.

2.2 Causes for microcredit default

“Microfinance may be one of the world´s most powerful new solution[s] to poverty, as

well as to the wars, diseases and suffering that poverty ignites […] if it works” (Datar,

Epstein and Yuthas, 2008).

Ming – Yee (2007) developed a research called “The International Funding of

Microfinance Institutions: An Overview” where he analyses the MFIs differences around

the world. This work highlights a very interesting point. In addition to the immense

diversity of MFIs in the world, there are important differences by region.

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Ásia and Pacífic countries: Microcredit focuses on the rural poor and grants

credit to micro-enterprises.

Latin American countries: Microcredit tends to be developed by formal and

regulated entities.

Middle East and North African countries: There are many non-governmental

organizations and some cooperatives supporting microcredit.

In this research, Hsu Ming Yee (2007) also refers to an important point regarding uneven

opportunity: “Within each region, wide disparities can exist between individual

countries. In Latin America, microfinance is well developed and competitive in small

countries like Peru and Bolívia, but it is less widespread in large countries like Brasil and

Mexico.” This is one of the interesting points to be considered when studying

microcredit default. With growing participation of commercial banks, a question can be

raised: how they will ensure the return rates on microcredit.

Roslan and Karim (2009) did a study on the Agrobank case, which is dedicated to

conducting lending in the agricultural sector in Malaysia. They identified the main

default determinants and reached some conclusions. The probability for loan repayment

default is influenced by the following:

o gender (males are more likely to default)

o type of business activity (commercial activities are more likely to default

than services activities)

o loan amount (the lower the loan, the higher default)

o repayment payment (the longer the repayment, the higher the default)

o training (the lesser training, the higher default)

Microfinance institution performance on loan repayment has been considerably very

good. Roslan and Karim (2009) mention in their article that it has been reported that the

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loan repayment rates of Grameen Bank in Bangladesh are almost above 95% (which

follows Morduch, 1999). In Malaysia, the repayment rates of Amanah Ikhtiar Malaysia

(AIM), which is a modified replication of the Grameen Bank, is about 97% (BNM, 2006).

Deininger and Liu (2009) examined how repayment is affected by loan source,

group provision of public goods, and imposed management practices. They highlight the

importance of rules, regular monitoring and audits for the decrease of repayment

probability.

Ledgerwood (1999) came up with six essential elements in order to manage loan

difficulties:

i) The credit service must be valued by clients.

ii) Clients must be screened carefully.

iii) Field Staff and clients must understand that late payments are not

acceptable.

iv) Microfinance institutions need accurate and timely management

information systems.

v) Loan difficulties need effective follow-up procedures.

vi) The consequences of loan default must be sufficiently unappealing to

clients (such as legal actions, visits by debt collectors, penalties and public

announcements).

Oke, Adeyemo and Agbonlahor (2007) analyzed the socio-economic variables

that affect microcredit repayment of non-governmental organization clients from

Nigeria (where repayment rates were about 90%). Oke, Adeyemo and Agbonlahor

(2007) identified some variables that may influence repayment such as follows:

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family income

distance between dwelling place and bank

amount of business investment

social-cultural expenses

amount of loan borrowed

access to business information

poverty indicators (poverty was found to hamper repayment)

Cheung and Sundaresan (2002) developed an interesting model that delivers

several implications for the role of monitoring by lenders that “shows that peer

monitoring combined with a limited amount of monitoring by lenders increases the cost

of borrowing, and this might lead to non-participation by borrowers.” As the loan size

increases, their model shows that the probability of default increases, and the loan rates

dramatically increase, unless the maturity of the loans is increased.

Bhatt and Tang (2007) researched on determinants of repayment in microcredit

based on the United States Program. In their paper they say that “some programs have

achieved high repayment rates (Else and Clay-Thompson, 1998); some have been

plagued by high loan losses (Edgcomb et al., 1996).”

Elisabeth Rhyne (2001) did a case study on how flourishing Bolivian microfinance

institutions attracted public anger in the early 90´s. She analyzed the rise of consumer

credit that led to an over-indebtedness crisis during 1999 and 2000. Bolivian borrowers

started “bicycling” loans (using the proceeds of one loan to pay off another). The Debtor

´s Revolt started in which they demanded full debt forgiveness. These happenings in

Bolivia were able to show the fine line between social microfinance and purely profit

MFIs. Another study made by Bwonya-Wakuloba (2007) identified the causes of default

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in government microcredit programs (slow repayment and high default rates). The main

cause of default was found to be poor business performance followed by domestic

problems (borrowers used the funds for other unprofitable uses, but other

determinants were pointed to as causes for default, though on a smaller scale: poor

timing, tenancy problems, theft and business closure).

Breza (2010) wrote a paper where she estimated the effect of peer repayment

on an individual’s own repayment decision. She concluded that the “Microfinance

System that boasts near perfect repayment rates in good times can be very fragile in

response to crisis.”

Measuring default rates is hard. Microfinance institutions usually operate in

places where it is difficult to conduct research – places that are geographically isolated,

politically unstable, technologically backward and educationally disadvantaged (Datar,

Epstein and Yuthas, 2008).

Kurosaki and Kan (2011) researched repayment behavior of microfinance

borrowers in Pakistan. The authors analyzed the repayment behavior of microfinance

borrowers while faced with idiosyncratic and covariate shocks. In 2005, the area

considered in this study was hit by a natural disaster (earthquake), and the authors

found out that loan repayment was not significantly affected. That could be explained

by the ability of this particular microfinance institution to adapt, also reflecting a change

in lending strategy (after the earthquake this particular MFI tended to lend to

households with outside income). Repayment problems were controlled with selection

and monitoring. After the earthquake, microfinance institutions started to become more

selective when lending and when there were repayment delays. Kurosaki and Kan

(2011) showed that “widespread strategic default affects the sustainability of

microfinance more adversely than a purely covariate, negative shock such as the 2005

Pakistan Earthquake.”

Dixon, Ritchie and Siwale (2007) analyzed the recent repayment crisis felt by 11

CETZAM (an emerging Zambia microfinance institution) and the effects of this

repayment crisis on the strategies to deal with defaults. They pointed out the

importance of knowing what loan officers do, and how they manage default. Between

borrowers and loan officers, different expectations can be found, and this can influence

results, proper client orientation and timely disbursement. Joint liability and dynamic

loan incentive was created to encourage banks to extend loans to the poor (Bakshi,

2008).

However, Guttman (2007) observed that “when default is due to opportunistic

behavior by the defaulter, social sanctions are assumed to be sufficiently strong to

prevent default.” Default borrowers usually have to remain living in the same

community with which they defaulted, and sometimes desperate measures are taken.

“Rumors of microcredit crisis were reported on October 2010. They were mainly

originated in the Indian state of Andhra Pradesh. This finding prompted the local and

global community. First, we think it will be necessary to consider the context in which

this particular case happens. CGAP makes an interesting point ”1 This report about

Andhra Pradesh states that in India, less than one quarter of the population has access

to basic financial services. The authors of the report explain the onset of micro-credit, its

historical and political contours of development and what borders it has reached.

Andhra Pradesh is the fifth populous state of 28 states, with 75 million inhabitants.

Among several projects to combat poverty developed in Andhra Pradesh, the most

prominent is the Society to Eliminate Rural Poverty (SERP). It focuses on rural

development accompanied by the state government. Andrha Pradesh has initiated a

program called "Total Financial Inclusion Program," a program initiated by the

government three years ago. It is the center of microfinance in India. MFIs (microfinance

institutions) have captured interest with an external injection of capital. All this fervor of

international interest in MFIs created the perception of MFIs as organizations working

for profit. Some MFIs acted and still act responsibly; others have had incredibly high

1 Andhra Pradesh 2010: Global implications of the crisis in Indian Microfinance ”Focus Note 67” Washington D.C: CGAP November

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profits and have compensated their executives with high wages. They had no

transparency in their actions, which created a negative stereotype around MFIs.

Dokulilova, Janda, and Zetek (2009) identified the following problems that MFIs

will face in the actual financial crisis under five categories:

i) ethical reasons (corruption, lack of motivation)

ii) managerial reasons (lack of management, lack of training, poor record-

keeping, and low management capacity)

iii) legal reasons (different legislation for different MFIs)

iv) unfortunate reasons (flood, famine)

v) others (such as a gap between “dropping out” vs. “coming in” rates)

CGAP took a survey in March 2009 to monitor the recent crisis impact on MFIs. They

measure that 60% of the respondent MFIs (over 400) stated that their clients are finding

it harder to repay their loans. These findings were more revealing in two regions--ECA

(Europe and Central Asia – 75% of the respondents) and LAC (Latin America and

Caribbean – 67% of the respondents). The MENA region reported a lower level of

repayment problems among their clients (41%). The most affected activities are petty

trading, agriculture and manufacturing according to this survey.

Over the last decade, we have assisted an increasing development of

benchmarks for assessing efficiency and financial MFIs´ performance. MIX (Microfinance

Information Exchange) and the MicroBanking Bulletin are constructive efforts to

accelerate this important trend (Counts, 2008). “The most dangerous problem a

microcredit program faces is repayment default” (Bwonya-Wakuloba, 2007).

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The following table summarizes our review of the literature.

Table 1 – Literature Review – Authors and Variables

Author/Authors Causes for MicroCredit Faillure Variables we’ll use

Roslan and Karim 2009” Gender, repayment period, type of business activity, training, amount of loan

Small Firms

Deininger and Liu, 2009 Loan Source; Imposed Management Practices, Group Loan

Collateral

Cheng and Sundaresan, 2007 Microloan Rates, fixed rate loans, floating rate loans,nr, of borrowers, access to technology

Access to finance

Nitin Bhatt and Shui-Yan Tang, 2002

Borrowers gender; borrowers educational level, household income; degree of formality of borrowers business, nr. Of years borrowers in business, Proximity of lenders agency

%of firms negotiating with banks

Berg and Schrader, 2009 Credit amount, credit maturity, gender Women access to Bank

CGAP´s 2009 Opinion Survey Food Prices, Level of Impact, Liquidity Constraints , Funding Costs,

% Agriculture Employment

Dokulilova, Janda, and Pavel, 2009

Interest Rates, Structure of Institutions liabilities, Institution´s Financial State, Economic Health of borrowers, Funding.

GINI Index

Counts 2008 Interest rates, costs, profitability, health crises, MFI competition, Progress Out of Poverty Index (PPI)

Poor

Bwonya-Wakuloba 2007 Loan diversion, Domestic Problems, Numerous Dependents, Follow up, Timing, Business Closure

African Cases

R.Dixon, J.Ritchie, J.Siwale

2007

Proper client orientation and adequacy of loan disbursement increases repayment

African Cases

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In this time of challenge for the world economy, it will be interesting to monitor

how microfinance will be able to overcome credit default and try to answer the

following question: “What are the determinants for default?” We’ll try to answer in the

following sections.

3. Data, Model and Empirical Results on Microcredit Default

3.1 Data and Model

Through the literature review, we realized that interest rates are often analyzed in order to measure risk of default. It is assumed that higher interest rates can produce a higher number of default cases. Based on this assumption, we decided to base our model on the Yunnus (2007) methodology that divides countries by three colours--Red, Green and Yellow--according to the average performance of MFIs:

- Green Zone: The difference between MFIs’ interest rates and their cost of funds is less or equal to 10 percentage points.

- Yellow Zone: The difference between MFIs’ interest rates and their cost of funds is between 10 and 15 percentage points.

- Red Zone: The difference between MFIs’ interest rates and their cost of funds is more than 15 percentage points.

A database was built based on two sources. Our dependent variables are the percentage of green, yellow and red cases for each country (for instance, suppose that for a given country, the value for red is 5%; this means that 5% of the microcredit borrowers in that given country are operating with banks characterized by a difference between MFIs’ interest rates and their cost of funds of more than 15 percentage points).

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These data are from Mix Market (http://www.themix.org/publications/microbanking-bulletin/2011/01/sacrificing-microcredit-unrealistic-goals).

Our independent variables (causes for default) were gathered from Encyclopedia

of the Nations (http://www.nationsencyclopedia.com) using the dimensions suggested

in Table 1. Table 2 shows the descriptive statistics of our variables.

Table 2 – Descriptive statistics

Variable Nr.of Obs. Mean Std.Dev. Min Max

Gini Index – Income Distribution Poverty-World Development Indicators

86 42,57279 8,528956 16,83 59,5

Firms using Banks to Finance investment

81 20,98469 15,25792 0,87 74,36

Small Size (under 20) enterprise

82 61,22878 16,71182 8,69 93

Value of Collaterall Needed for a Loan

80 139,5006 50,32144 41,17 259,73

Woman Access to Bank Loans

64 0,275 0,294392 0 1

Income Share held by lowest 10% - Income Distribution Poverty

85 2,399059 0,9924496 0,6 6,11

Employment in Agriculture (% of total Employment)

74 36,89757 22,94325 0,8 90,1

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Africa Country 95 0,3578947 0,4819241 0 1

Access to Finance

81 15,75198 9,848612 1,97 47,87

Our empirical model is as follows:

(EQ.1)

(EQ 2)

(EQ 3)

3.2 – Results and Implications

As previously noticed, we are using the variables that the authors that we studied point to as most statistically interfering with different default rates on

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microcredit. For each equation, we are going to study the set of countries described by Mix Market (2010). We will employ Ordinary Least Squares as our estimation method, considering robust estimates for correcting problems of heteroskedasticity.

Our results are shown in Tables 3, 4 and 5. Table 3 shows different specifications estimated for Equation 1 (red cases). Table 4 and Table 5 show the same exercise for, respectively, Equations 2 (yellow cases) and 3 (green cases).

Table 3 – Results on red cases

RED (I) (II) (III)Gini 0.0049895

(0.0141119).0076571 * (.0042522)

0,0077466 *(0,0042014)

FirmBank -0.0142268 ***(0.0036977)

-0.0142896 *** (.0035591)

-0,01376 ***(0,0031382)

Small -0.006254 **(0.0025256)

-.0061899 **(.0024136 )

-0,0051 **(0,002396)

Collateral -0.003603 ***(0,0008806)

-.0036215 ***(.0008498)

-0,0032 ***(0,0007529)

Women Bank 0.27118 (0.1655133)

0.2738071 *(0.1582749)

0,3213**(0,1323679)

Poor -0.0257529 (0.1279595)

Agric -0.0007879 (0.0022453)

Cont1 0.1089714(0.100218)

0.1086129 (.097259)

Access 0.0001718 (0.0041516)

-.0009232 (.0020357)

Number of Observations

42 42 49

R2 0,6100 0,6094 0,5176Teste F 5,56 7,58 9,23

Table 4 – Results on yellow cases

YELLOW (I) (II) (III)Gini 56522.81 **

(20713.02)111146(66848.64)

41357,48 ***(13904,02)

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FirmBank 5237.988 (5952.821)

55563.9 **(23414.15)

Small 123.2321 (3041.531)

Collateral -698.6906(1174.085)

-1910.788 (4770.093)

Women Bank -180503.5 (276613.1)

981392.8 (978944.4)

Poor 409479.1**(190608.5)

1014745 *(576766.7)

305704,5 **(116960,2)

Agric 1009.345 (3955.021)

-5283,186 **(2284,758)

Cont1 77005.16 (147815)

132080.1 (662996.4)

209110,8 *(108091,5)

Access -4973.029 (5597.037)

-14388.11 (24642.18)

Number of Observations

26 29 44

R2 0,4247 0,3766 0,2739Teste F 1,31 1,81 3,68

Table 5 – Results on green cases

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GREEN (I) (II) (III) Gini 76334.51

(116375.3)

FirmBank -22346.17 (30137.83)

-25427.8 (22001.41)

Small -40656.52 (25275.1)

-51005.55 ** (20842.94)

-47119.47 ** (19774.13)

Collateral 4592.436 (6145.215)

10553.66 * (5280.054)

1440486 ** (640587.3)

Women Bank 901442.9 (1578513)

Poor 741370.7 (1053745)

86308.38 (223468)

Agric 30051.62 (22321.17)

26855.27 (16012.96)

34426.76 ** (14305.48)

Cont1 -3327417 ** (1204541)

-1925367 * (989794.6)

-1981838 ** (921755.7)

Access 166893.4 *** (44868.02)

94422.04 ** (34662.46)

92069.86*** (31984.3)

Number of Observations

24 32 32

R2 0.7457 0.5354 0.5231 Teste F 4,56 3,95 5,70

Table 3 results show that there is a higher number of red cases if there is a higher income inequality in the country (a positive coefficient estimated for the Gini Index), and whether there are higher levels of women using bank services. The percentage of red cases also tends to rise if we observe a smaller value of firms using banks to finance investments, if we observe a reduced expression of small firms, and if we have smaller values of collateral needed for a loan.

Table 4 shows the results obtained for yellow cases. In this table, we confirm that a higher level of population under the poverty line, a higher level of the Gini Index, and being an African country lead to higher levels of yellow cases. In a reverse direction, a higher level of the share of people employed in agriculture reduces the percentage of yellow cases.

We have the results for the equation related to "green" cases on Table 5. According to these results, increasing the access to finance, the percentage of people employed in agriculture, and the value of collateral needed for a loan leads to higher

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values of green cases. By a reverse direction, a higher value of small firms and being an African country reduces the percentage of these best cases.

Some comments can be generated according to our results. First, a higher number of small firms tends to reduce the risk of default (“red” interest rates, usually observed more often in realities characterized by a higher percentage of large firms), but by a more statistically significant result, it also tends to reduce the “green” interest rates. This shows us that if we are interested in preserving traditional microcredit characteristics, we have to develop an economic environment able to support small firms.

Secondly, we have also observed that increasing the agriculture sector leads to a rising of “green” cases. This result puts special attention on the trend of tertiarization (prevalence of the tertiary sector in economies). As we can anticipate this trend for most countries, this can lead to a devaluation of “green” interest rates and lastly to a devaluation of microcredit practices, changed into conventional credit (based on only-profit-maximization practices).

Finally, a word to collateral values is deserved. Our results suggest that higher collateral values promote more “green” cases. Thus, we can recognize that good management of collateral practices (using, for instance, group or family collaterals) can be relevant for sustaining microcredit goals.

We also observed that being an African country tends to increase red and yellow cases. This fact signals the urgency of monitoring African cases of microcredit that can have very little microcredit original inspiration.

Our results also lead to important political implications on managing microcredit practices. First, we have to develop the dimensions that shall increase green cases. For these cases, we have to promote policies that increase bank access, agriculture sustainability, and the value of the collateral needed for a loan. Secondly, we have to try to reduce the dimensions that we observed as improving red cases. We have to try to reduce income inequality in the country and expand the number of small firms and/or entrepreneurship. Finally, yellow cases can be observed as transition cases--as grey areas between light and dark examples. Therefore, yellow cases deserve a particular focus. As we observed, the higher the level of population under the poverty line, the higher the level of the Gini Index. Furthermore, being an African country lends itself to higher values of yellow cases. Therefore, in order to make the transition of these yellow cases toward green cases, we have to fight against poverty and income inequality.

4. Conclusion

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Microcredit is practiced worldwide. The Asia and Pacific region concentrates the largest number of microfinance institutions (MFIs)--22% percent of the world’s population and 25% of the world’s poor.

Higher microcredit interest rates in some areas may not only say that MFIs operating in the area are profit-seeking but may also reflect the political instability of the area and reflect the risk to default.

Through our research, we discussed several determinants that were found to influence microcredit default cases such as gender, type of business activity, loan amount, repayment payment, training, corruption, follow-up practices, business performance, family income, poverty indicators, and domestic (national) problems.

For our model we took into consideration the following variables: income-distribution poverty, firms using banks to finance investment, small-size enterprise, value of collateral needed for a loan, women’s access to bank loans, employment in agriculture, access to finance and being an African country.

We divided the countries around the world, taking into consideration the Yunnus (2007) taxonomy: red cases, yellow cases, and green cases. Red cases point out the countries with the largest number of microfinance institutions operating pro-profit (showing a large difference between microcredit interest rates and microcredit costs). At the other extreme, green cases signal the countries whose microfinance institutions reduce the difference between microcredit interest rates and costs to a minimum. We also assumed that higher interest rates shall lead to a higher value of defaults. Our results showed that green cases are increased when more collateral value is required and the country is not African. Higher levels of the population under the poverty line, higher levels of the Gini Index, and being an African country lead to higher levels of yellow cases. We observed that the percentage of red cases tends to rise if we have a smaller value of firms using banks to finance investments, if we observed a reduced expression of small firms, and if we had smaller values of collateral needed for a loan.

Being an African Country showed itself to be a strong factor in our model. Africa is characterized by political instability. Even with these difficulties, microfinance has begun to blossom in African countries (Moyo, 2009). As Moyo (2009) says, even though sometimes microcredit is criticized for its rates and Ponzi schemes (borrowing from one lender to pay off another), the fact is that in Africa, “joint liability” schemes work, and the number of loans have increased. Microcredit has the power to accelerate growth in developing countries such as African countries that need a more innovative financial sector and special supervision (as we claim in this article).

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By way of summary, in order to preserve and/or develop microcredit practices considered as “green” cases, it is necessary to be prepared for the challenges of the tertiarization of economies and promote higher values of collateral needed for a loan. On the other hand, it is crucial to promote policies leading to micro- and small-entrepreneurship in order to avoid microcredit practices degenerating into pure examples of profit maximization.

References

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