Does efficiency lead to lower interest rates? A new

perspective from microfinance

Ahmad Nawaz1, Marek Hudon2 & Benanzir Basharat3

1Pakistan Institute of Development Economics,2 CEB (SBS-EM), Université Libre de Bruxelles

(ULB); CERMi; Burgundy School of Business & 3 International Islamic University, Islamabad

Abstract:

This paper provides first empirical evidence of the role of efficiency in microfinance in the

determination of interest rate charged to the clients. Using the data of 291 MFIs in 67 countries

worldwide, the results confirm that after controlling for loan size and gender, social efficiency has

insignificant impact on interest rate which depicts the fact that as outreach increases, it causes the

lending interest to rise. On the other hand, financial efficiency of MFIs has significant positive

relation with interest rate even after controlling for outreach, gender and ROA.

Key Words: Microfinance, Public Policy; Performance, Interest rates

JEL Codes: G21, L38, L25,

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1. Introduction

The microfinance sector has grown dramatically for the last ten years. The main objective

of MFIs is to provide affordable financial services to poor and low income households. Interest

charged on loans is justified by MFIs as it is the main source of income for these institutions. MFIs

claim that they have to provide financial services to poor people and have to face administrative

cost that’s why they charge such high rates1. It is generally hypothesized that an effort to improve

financial performance results in raising interest rates on loans but at the same time it inversely

affects the social performance (Copestake, J. 2007). However, empirical evidence about the role of

efficiency in the determination of interest rate in microfinance is relatively scarce in the literature.

Cull et al. (2007) using data on 124 institutions in 49 countries provided evidence that raising

interest rates to very high levels does not ensure greater profitability and the benefits of cost-

cutting diminish when serving better-off customers.

There exist many factors can have either a direct or an indirect impact on microfinance

interest rates. As pinned down by Campion et.al (2010), perhaps the most important is the

improved operational efficiency—a key driver of lower rates—comes primarily from five sources:

competition, reinvestment of profits, learning by doing, pressure from donors and investors on

MFIs to be socially responsible, and the absence of interest rate caps.

This paper fills this gap by investigating the determinants of interest rates by incorporating

both social and financial efficiency of microfinance. To analyze the financial efficiency, we employ

the non-parametric DEA framework which is very common in the banking literature

1 Nimal A. Fernando (2006), “Understanding and Dealing with High Interest Rates on Microcredit, A Note to Policy Makers in the Asia and Pacific Region”

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(Athanassopoulos, 1997; Seiford and Zhu, 1999; Camanho and Dyson, 2005). On the other hand,

following Nieto et al. (2009), we measure the social efficiency by including number of poor and

women borrowers as an output into the DEA framework. Hence, notwithstanding the inclusion of

social and financial efficiency in the analysis, empirical evidence has been investigated by

employing those efficiencies as drivers of the interest rates in microfinance.

This quality financial information has been generated directly from the Mix Market2

website for 291 MFIs located in 67 Countries for four years (2005-2008) thus totaling to 1167

observations. Once the financial and social efficiencies have been calculated, the study aims to

highlight some key issues in the public policy debate in microfinance. Notwithstanding the MFIs

location, status, lending methodology, regulations, which are more socially efficient and which are

more financially efficient? The study further investigates some specific hypothesis about the role

of efficiency of microfinance in the determination of interest rates in microfinance. We will also

test if outreach, the ownership structure, the size and the percentage of women served by the

MFIs have an impact on the interest rates.

Our empirical results based on the panel data suggests that social performance of MFIs has

significant inverse relation with interest rate but after controlling for loan size and gender, it has

positive but insignificant impact on interest rate which depicts the fact as outreach increases, it

cause the lending interest to rise. On the other hand, financial performance of MFIs has significant

positive relation with interest rate even after controlling for ROA.

The paper is organized as follows. In the next section, to start off, we review the literature

on efficiency and public policy in microfinance. In Section 3, we describe the theoretical

background of non-parametric financial and social efficiency analysis. We then present in Section 4

the database and some basic descriptive statistic. Section 5 provides financial and social efficiency

2 The audit reports have been taken from the Mix Market Website (hhp://www.mixmarket.org). The MIX MARKET is a global, web-based microfinance information platform. It provides information to sector actors and the public at large on Microfinance Institutions (MFIs) worldwide, public and private funds that invest in microfinance, MFI networks, raters/external evaluators, advisory firms, and governmental and regulatory agencies

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results. Section 6 highlights the empirical evidence by employing the regression analysis. Finally, a

conclusion is given at the end.

2. Efficiency and Public Policy in Microfinance

Although public policy has been largely debated in the microfinance sector, the nexus between

interest rates and efficiency of microfinance is largely a neglected area of research. Higher interest

rates in microfinance have spurred lot of debate recently in public policy issues in microfinance.

Generally the nominal interest rate charged by most MFIs in the all the regions on average falls in

the range is 30% to 70% a year. According to the Mix Market database, the annual lending interest

rate charged by microfinance institutions during the period 2000-2008 was on average 42% in

Africa and Latin America and 35% in Asia. Since annual inflation rates during those years in all

three continents were around 7%, real interest rates paid by microfinance clients were high. Thus,

it is not surprising that one of the most discussed issues in microfinance is the high interest rates

these institutions charge. As Gonzalez (2010) asks, “are high micro credit interest rates not a sign

that these institutions that proclaim development objectives are in fact gouging the poor?”

An empirical study to investigate this issue is of Cotler, P., and (2010) who tried to find out

the main determinants of this high interest rate. By applying regression approach, they used the

data of 1299 MFIs of 84 countries from Africa, Asia and Latin America. By using ROA as indicator of

profitability, average loan size for outreach operating cost for productivity and funding rate of

interest as liability of MFIs, he found that both operating cost for small loans and ROA equally

significant in determining the high rate of interest. Funding rate is also a contributor of this lending

interest rate and on the other hand, age is inversely related with this interest depicting the fact

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that as these MFIs matures they are better able to control their operating cost and thus to lending

rate of interest.

Like the conventional financial institutions, the efficiency and productivity of MFIs has

generally been measured by conventional financial ratios or indicators such as staff productivity or

operating expense ratio (Balkenhol, 2007). An example is Hudon and Traca (Forthcoming) who use

staff productivity. Most new studies on cost efficiency use more sophisticated indicators of

efficiency such as data envelopment analysis (DEA) or stochastic frontier analysis (SFA) to calculate

this frontier. Hermes et al., (forthcoming) estimate efficiency of 435 MFIs with stochastic frontier

analysis and find that outreach and efficiency of MFIs are indeed negatively correlated, what

suggests some trade-off between these dimensions. Gutiérrez-Nieto et al. (2007) use DEA to a

sample of 30 Latin American MFIs to test twenty-one specifications. They apply principal

component analysis to explain efficiency scores by means of four principal components. Their

results show different rankings using DEA and more conventional benchmarks and financial

indicators.

However unlike conventional banking institutions, MFIs have a social face too and donors

give importance to their social aspect with financial aspect when it comes to disbursement of

funds. Therefore, performance of MFIs should not be solely gauged by financial indicators but it

should take into account their social performance too i.e. outreach to poor. As argued by

Balkenhol (2007) that efficiency should be regarded as a key indicator by donors who should not

only focus on financial but also social performance.

In microfinance literature, there exist studies focusing on the financial efficiency but none of them

consider the social efficiency aspect. The very first effort in this regard is made by Gutierrez-Nieto

et al. (2009), which have focused on financial efficiency of MFIs by adding the analysis of social

efficiency of these institutions. Their study was aimed to show the relationship between social and

financial efficiency, and the relationship between efficiency and other indicators, such as 5

profitability, age and type of institution such as Non-Governmental Organization (NGO) and non-

NGOs. Data of 80 MFIs were taken from Mix market webpage for 2003.Among the 89 MFIs in the

study, only 13 of them were found socially efficient than financially efficient, 37 NGOs were more

socially efficient than non-NGOs. So, they concluded that MFIs chose between financial efficiency

and social efficiency they prefer to be financially efficient so they can better perform their social

work.

To fill in the vacuum by incorporating the complete picture, this study goes beyond and

examines the relationship between interest rate and social efficiency, in addition to the financial

efficiency.

3. Efficiency: Theoretical framework

3.1Financial Efficiency

For the efficiency analysis of the microfinance institutions, a two-stage analysis has been carried

out. Data Envelopment Analysis (DEA) approach is used to estimate technical and pure efficiency

scores of the MFIs. The advantages of using the DEA technique to gauge efficiency are well

documented in the literature. DEA framework can handle multiple outputs and inputs. Thus, in the

context of MFIs efficiency analysis, it can incorporate both the outputs of outreach and

sustainability along with other inputs into a single framework. Neither has it required any price

information for the dual cost function nor parametric functional form for the production function.

Insert Table 1 here

Table 1 depicts the summary of inputs and outputs selected for this study. The main

objective of estimating a production function is to explain the quantity of output produced given

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certain levels of inputs and other relevant factors that might explain the quantity of output

produced. In traditional financial literature two models i.e. Production Model and Intermediation

Model are popular depending upon what one thinks an institution do. The majority of the studies

in banking efficiency literature are based on the input oriented constant returns to scale CCR

model (Charnes et al, 1978). In the production model approach, financial institutions are treated

as firms that use physical input, employees and expend money in order to obtain deposits, grant

loans and collect revenues. We assume the output oriented Production model with variable

returns to scale is better suited to microfinance institutions rather than constant returns to scale

model. MFIs are indeed interested in increasing outreach i.e. lending loans to poor people which

commensurate with not only their social mission but also contributes towards sustainability as

well by collecting more revenues from lending. In addition to that they compete in an imperfect

economic environment as the markets for MFIs are not as well developed as the conventional

banking sector. And they always have restricted amount of money and human resource (Inputs) to

spend on unlike commercial banks which can generate money from shareholders. In the context of

output oriented model, this essay asks a specific question “By how much the output quantities are

proportionally expanded without altering the input quantities used?”

The selection of specifications with correct inputs and outputs in the context of MFIs is very

important. Based on the literature, we have selected a few inputs and outputs. This study uses LR-

ACE3 as a general specification where gross loan portfolio and financial revenues are taken as an

output and assets, operating costs and number of staff as an input. In addition to that, we have

also used specifications L-ACE and R-ACE, where the former put emphasis on granting loan as main

objective of MFIs and latter signifies revenue collection as main objective of MFIs. The other

specifications used are basically the different combination of treating subsidies as an input and

output with the above general specifications.

3 The left part in all the specifications show outputs and the right part depict inputs.

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3.2 Social efficiency

Following Nieto et al. (2009), on top of the traditional variables used in DEA specification, we also

include two output variables to calculate the social efficiency of microfinance i.e., variable P which

indicates the benefit to the poorest and number of women borrowers. The main purpose of

establishing microfinance institutions was to fight against poverty but the problem is how to

measure poverty and how these poor can have access to micro credits? ‘Average loan balance per

borrower’ is an indicator which is used as a measure outreach, smaller the average balances of the

loan, the deeper the reach of the microcredit (Olivares-Polanco (2005), Cull et al., (2007), Roy et al.

(2009)). But average loan balance per borrower is criticized as indicator of outreach as it is

measured in monetary units and may mean different in different countries depending on their per

capita income (Morduch (2000)). Therefore it is taken in relative terms by dividing it by the per

capita Gross National Income (GNI pc). According to WDI, “GNI is the sum of value added by all

resident producers plus taxes (less subsidies) not included in the valuation of output plus net

receipts of primary income from abroad”. For this study, GNI pc is included by calculating it

through purchasing power parity (PPP) method. PPP convert GNI pc to international dollars using

purchasing power parity rates. It is directly taken from the WDI4. PPP method has an advantage

that it provides better information of a country's international purchasing power and their relative

economic strength. The method provides better information of the living standards of developing

countries5. By denoting the relative term with K:

K = Average loan balance per borrower/ GNI pc (1)

Higher the value of K, the larger the average loan size in relative term. We assign K the range

between 0-1 and thus find a value between 0 and 1 where a value near 0 indicates that the

4 world development indicator5 http://en.wikipedia.org/wiki/Gross_domestic_product and Cypher, M., James and Deitz, L., James (2008), “Process of economic development” books.google.com.pk/books? isbn=041577103X

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institution lends to the poorest. In order to find the objective of reaching poor (pi) we deduct K-

min (K) from 1 as follows:

pi = 1 – (Ki – Min (K)/Range (K)) (2)

Where i is an indicator associated with a particular MFI. Min (K) is the minimum value over all i,

while the Range (K) is the maximum value of Ki minus the minimum value of Ki. For every MFI, we

multiply p by the number of active borrowers in order to construct the poverty indicator (P).

P=pi*number of borrowers (3)

4. Database and descriptive statistics

Our data come from the Audit Reports of MFIs taken from Microfinance Information

eXange Inc. website. Through this information exchange platform, individual MFI can provide

financial and outreach data and the Mix Market ranks these data for quality using a 5-star system,

where 5 is the most complete data available, while 1 is the least complete data available (usually

the number of borrowers and some other outreach indicators but little financial information). Only

5-star MFIs, which include MFIs reporting audited financial statements and a Rating on their online

profile, have been incorporated. As most samples using databases from rating agencies or Mix

Market, our sample is probably biased versus the largest and better managed around the world.

Given the well-established concentration of microfinance clients in the largest institutions

(Honohan, 2004), the sample of 291 MFIs in 67 countries in the underlying study is however

representative of the universe of microfinance activity.

A balanced panel data consists of total 1164 observation (291 for each year) is generated and it is

being restricted to only observations with non-missing values for four years (2005-2008). The

rationale for despising an unbalanced panel of data is to minimize the noise in the data mainly due

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to outliers6.Our sample for this essay consists of total 340 observations (170 for the each year

2005 and 2006) which is assumed to be representative of the whole microfinance sector. For

example, the 1,084 MFIs reported in the 19th MicroBanking Bulletin [MBB] (MicroBanking Bulletin,

2009) yield a median Operational Sustainability of 111% compared to ours of 116.27%. The

median nominal yield is 31% in the MBB and 30.54% in our database. Table 2 presents a summary

statistics of the variables used as an inputs and outputs in the DEA framework along with other

social and organizational variables used in the regression framework.

INSERT TABLE 2 (Appendix)

Figure 1 depicts the pictorial analysis of our data set. 50% of our total MFIs are registered as NGO,

followed by the NBFIs (29%). MFIs with “Bank” status constitute only 9% of total sample. More

than half of the MFIs (55%) offer both group and individual lending services followed by MFIs

which lend exclusively to the groups (21%). Geographically more than one-third of MFIs locate in

Latin America (37%) and almost one-fifth in Africa (20%). South Asian MFIs constitute only about

13% of the total MFIs in the sample.

INSERT Figure 1 (Appendix)

5. Efficiency Analysis

Table 3 presents the financial and social efficiency analysis of the MFIs bifurcated into

various categories. The scores of financial and social efficiency have been calculated using

constant returns to scale efficiency (crs), variable return to scale efficiency (vrs) and scale

6 See for detail Verbeek, M., and Nijama, T., (1997), Bruno, G., (2004), Yaffee, R., (2000), “A Primer for Panel Data Analysis” and Woolridge, J. (2002), “Econometric Analysis of Cross-Section and Panel Data”. MIT Press, pp. 130, 279, 420-449.

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efficiency (Scale). However in the Table 3, only the average efficiency scores for specification LR-

ACE (financial efficiency) and WP-ACE (social efficiency) have been presented by employing much

realistic output oriented model with variable returns to scale.

Insert Table 3 Here

Financially, MFIs located in East Asia and Middle East are on average more efficient as compared

to other regions while African MFIs are the worst one relatively. Whereas, socially, East Asian and

South Asian MFIs are most socially efficient while African and Latin American MFIs are the worst

one relatively. As argued by Nieto et al., (2009) that MFIs which operate in different continents

adapt to the environment in which they work. Notwithstanding MFIs status, Banks are found to be

more financially efficient (Haq et al., 2008) while NGOs are socially efficient, this is same as the

result of Nieto et al., 2009 which concluded that average social efficiencies are higher for NGOs.

Insert Figure 2 here

Regarding lending methodology, MFIs who lend to individuals are more financially efficient,

as the study of Cull et al. (2007) concluded that MFIs that focus on providing loans to individuals

perform better in terms of profitability because individual-based MFIs, when they grow larger or

focus more on commercialization then they focus increasingly on wealthier clients and provide

large loans thus individual lending increases their financial performance. On the other hand, the

results of this study suggest that MFIs who provide loans to group and with village banking

methodology are more socially efficient supporting the view that this lending technique helps

reducing information costs related to lending to the poor more than other lending techniques do,

so its an effective way of reducing screening and monitoring costs of the MFIs (Hermes et al., 11

2009)7. With respect to type of MFIs, it has been argued that regulation of microfinance is

necessary8. Hartarska, 2005 and 2009 have concluded by using small sample of MFIs in Eastern

Europe and Central Asia that regulatory status of MFIs have no impact on financial sustainability 9

which confirms the results of this study, that regulated and unregulated institutes are equally

financially efficient. Yet, regulation may divert attention away from outreach, if regulatory

requirements focus too much on financial goals such as financial sustainability etc., as Makame

and Murinde (2006) and Barry et al., (2009), found evidence for a negative relationship between

regulation and outreach which confirms the findings of this study that unregulated MFIs are more

socially efficient. Moreover, MFIs which provide non-financial services are more socially efficient10

while MFIs which do not provide other services like education and health etc. are more financially

efficient. Regarding the savings, MFIs having saving design are socially less efficient than the one

with n saving features. While financial efficiency remains the same on average for both MFIs with

and without savings.

6 Linkage between interest rates and efficiency

Primarily, MFIs have two main objectives i.e. to achieve their social mission and to become

financially sustainable. Some of MFIs focuses on their financial sustainability goal, but many see

their social mission as more important than their financial objectives. In the 1990s, the importance

of financial sustainability of these institutions started the important debate between the financial

approach and the poverty lending approach (Robinson, 2001). The financial approach emphasizes

the importance of financial sustainability of MFIs and the poverty lending approach concentrates

on using credit to reduce poverty. The most recent microfinance pattern favors the financial

7 Cull et al. (2007) and (Hermes et al., 2009)8 (Hardy et al., 2003; Marulanda and Otero, 2005)9 (Hartarska (2005, 2009)) and (Mersland and Strom (2009))10 See Barry et al., (2009)

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approach. The main argument to support this view is that “large-scale outreach to the poor on a

long-term basis cannot be guaranteed if microfinance institutions are incapable of standing on

their own feet” (Herms, et al. 2007). Therefore, it is hypothesized that in order to remain socially

efficient, an MFI has to be financially efficient (Nieto, et al. 2009).

This study has also focused on one of important aspect of rate of interest charged by MFIs.

Generally the nominal interest rate charged by most MFIs in the region range is 30% to 70% a

year. According to the Mix Market database, the annual lending interest rate charged by

microfinance institutions during the period 2000-2008 was on average 42% in Africa and Latin

America and 35% in Asia. Since annual inflation rates during those years in all three continents

were around 7%, real interest rates paid by microfinance clients were high. Thus, it is not

surprising that one of the most discussed issues in microfinance is the high interest rates these

institutions charge. As Gonzalez (2010) asks, “are high micro credit interest rates not a sign that

these institutions that proclaim development objectives are in fact gouging the poor?”

The main objective of MFIs is to provide affordable financial services to poor and low income

households. Interest charged on loans is justified by MFIs as it is the main source of income for

these institutions. MFIs claim that they have to provide financial services to poor people and have

to face administrative cost that’s why they charge such high rates11. It is generally hypothesized

that an effort to improve financial performance results in raising interest rates on loans but at the

same time it inversely affects the social performance (Copestake, J. 2007).

INSERT TABLE 4 (Appendix)

11 Nimal A. Fernando (2006), “Understanding and Dealing with High Interest Rates on Microcredit, A Note to Policy Makers in the Asia and Pacific Region”

13

Overall, the correlation coefficients remain relatively low as depicted in the table above. They do

not exceed 0.8, the level at which collinearity problem appears (Kennedy, 2008). The correlation

between social efficiency (ACE-WP) and financial efficiency (ACE-LR) is 0.227. This is significantly

different from zero at the different levels (two way test), but it is rather small. The highest

correlation is between ACE-WP and loan size/GNI pc is (-62.02) which shows that smaller the loan

size higher will be the social efficiency and vice versa. The table also shows that profit motive,

smaller loan size and gender have almost same correlation with lending interest rate which means

that these three factors are of same importance as the determinants of high lending interest rate.

7 Tobit Regression Approach

7.1 Methodology

Once the financial and social efficiency scores are calculated, a panel Tobit regression analysis is

carried out to test a series of hypothesis concerning the relationship between financial and social

efficiency and other indicators in their respective sections below. The most-often encountered

approach to modeling the DEA scores against exogenous variables is Tobit regression, which is

suitable when the dependent variables are either censored or corner solution outcomes, of which

DEA scores falls within the second category. A corner solution variable is continuous and limited

from above or below or both and takes on the value of one or both of the boundaries with a

positive probability. As DEA efficiency scores are continuous on the interval [0; 1], and takes on the

value 1 with positive probability, it seems obvious to use a two-limit Tobit technique for modeling

the scores as a function of the exogenous variables. Tobit has as such been adopted as the natural

‘choice’ for modeling DEA scores in second stage evaluations. “The two-limit Tobit technique is

however miss-specified when applied to DEA scores, given that these only take on the value 1 with

positive probability (and not the opposite limiting value 0). Even so tobit yields sensible results in

second-stage DEA, and it has as such never been questioned whether Tobit is actually the most 14

appropriate method, regarding predictability of scores and effects of the exogenous variables”12.

“Tobit Model is an econometric model proposed by James Tobin (1958) to describe the

relationship between a non-negative dependent variable “yi” and an independent variable (or

vector) “xi”. The model is truncated when some observations on both the dependent variable and

regressors are lost and censored when data on the dependent variable is lost (or limited) but not

on the regressors”13.

The structural equation in the Tobit model (censored normal regression model) is:

𝑦i*=β Xi + εi

𝑦i* is a latent variable that is observed for values greater than 0 and censored otherwise. The

observed yi is defined by the following measurement equation:

yi = 𝑦* if β Xi+ εi >0

yi =0 if β Xi+ εi ≤0

yi is a dependent variable and this variable linearly depends on Xi via a parameter (vector)

β. ε ~ N (0; σ2), εi is a normally distributed error term to capture random influences on this

relationship. So, Tobit censored regression model14 will be used to test the effect of different

indicators on financial and social efficiency by taking these DEA scores as dependent variable.

Tobit censored regression can accommodate the censored DEA efficiency score since the values of

the dependent variable lie between 0 and 1 with some values achieving the highest value of 1

((Ray 1991, McCarty and Yaisawarng 1993, Lovell et al 1994 and Martínez-González A. 2008) . A

random-effect Tobit model has been employed to analyze the panel data. It should be noted that

Honore (1992) has developed a semi parametric estimator for fixed-effect Tobit models but there

does not exist a sufficient statistic allowing the fixed effects to be conditioned out of the

likelihood. Therefore, unconditional fixed-effects estimates are biased (Stata 9 manual).

12 McDonald, F., John and Robert A. Moffitt 1980, Hoff , A., 2007 and McDonald, F., John 200813 Wooldridge (2002), Greene, W. (2003), (Sigelman, L. & Langche Z. (1999)) and (Long 1997)14 The Tobit regression is applied when the dependent variable is censored that it is observed only if it is above or below some cut off level (Greene, W. (2003) and (Long, 1997))

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7.2 Results

7.2.1 Relationship between Lending Interest Rate and social efficiency

The following regression equation (4a) aims to investigate the determinants of high interest rate.

Does the interest rate follow the rates which financial institutions pay for their funding? Or are the

small loan size the main cause? Or profit motive determine this high interest rate? Four key factors

stand out in the determination of these rates: the cost of funds, the MFI's operating expenses,

loan losses, and profits needed to expand their capital base and fund expected future growth15.

Log ILi = α + β1log IF

i + β2log SocEffi + β3log ROAi + β4log Outreachi + β5log Agei + β6 log womeni +

µci + εi (4a)

Where Log ILi is the logarithm of lending interest rate, Log IF

i is logarithm of funding interest rate,

log SocEffi is logarithm of social efficiency, log ROAi is logarithm of return on asset, log Outreachi is

outreach indicator (loan size divided by GNI per capita in purchasing power parity); log Agei is the

logarithm of the number of years since the MFI has been operating and β5log womeni is the

logarithm of the percentage of female borrowers, for MFI i.

Ci are the controls for Region, Status, Lending Methodology, Saving, Regulated and Other services

i.e. health, education etc in addition to providing financial services or not. Geographic region in

which the MFI operates are classified into 6 regions: Africa (A); East Asia and the Pacific (EA&P);

Eastern Europe and Central Asia (EE&CA); Middle East and North Africa (MENA); Latin America and

the Caribbean (LAC); South Asia (SA). Lending methodology is classified into 4 categories:

Individual (I); Individual & Solidarity/Group (IS); Group/Solidarity (S); Village banking (V). MFIs are

15 Nimal A. Fernando (2006), “Understanding and Dealing with High Interest Rates on Microcredit, A Note to Policy Makers in the Asia and Pacific Region”, Rosenberg et al. (2009) and Cotler P. (2010)

16

classified into 5 categories of ownership structure: Nongovernmental organizations (NGO); Bank

(B); Non-banking financial intermediaries (NBFI); Rural Bank (RB); Cooperatives (Coop.).

The omitted variable categories are: for region, Africa; for status, Banks; for lending

methodology, Group lending; and others are MFIs with no saving feature, not regulated and no

other services.

Insert Table 5 here

Table 5 consists of total 4 equations bifurcated into 2 regression each (with social and financial

efficiency as left hand side variable respectively). Equation 1 comprised of all the determinants of

interest rate, whereas in equation 2, we control for outreach and in Equation 3, gender has been

controlled. Last but not the least, in equation 4, we control for the ROA.

Empirical results of Equation 4(a) with social efficiency as independent variable above have been

presented in regression equation 1(i), 2(i), 3(i) and 4(i) in table 5. In 1(i), social efficiency of MFIs

has found to be inversely related to the interest rate and the relationship is significant. However

after controlling for loan size and gender in equation 2(i) and 3(i) respectively, the relationship

becomes positive though insignificant. Which suggests the fact that as outreach increases (lower

loan size), it cause the lending interest to rise. ROA has a significant positive impact on interest

rate in line with the findings of Rosenberg et al. (2009) and Cotler P. (2010). Thus if MFIs increase

their profits and hence ROA, it will increase their lending rate. In line with the findings of Cotler P.

(2010), funding interest rate is found to have a significant and positive relationship with lending

rate of interest. On the other hand, our outreach variable as proxy for the average loan size has a

significant inverse relationship with lending interest rate even after controlling for ROA in equation

4(i). This suggests the fact that the smaller the loan size is, the higher is the rate of interest 17

charged to the clients. This result is also in line with the findings of Rosenberg et al. (2009) and

Cotler P. (2010). The reason for high interest rate for small loan size is high administrative costs

which consist mainly of rent, utility charges, transport, and depreciation of fixed assets and

recovering small loans. Often loan recovery is executed by staffs who visit clients and

transportation is used. Poor physical infrastructure and transportation in many countries in which

micro lenders operate also increases administrative costs (Nimal A. Fernando (2006)). Similarly

gender variable as measured by the percentage of women borrowers has a positive significant

relationship with interest rate even after controlling for loan size in equation 2(i). This confirms the

fact that the MFIs which predominantly lend to women borrower, charge higher interest rates to

their clients because of small loan size. Age has insignificant inverse effect on interest rate in first

equation but become significant in all four equations.

Notwithstanding the dummy variables, MFIs with status of cooperatives NBFIs, NGOs and Rural

banks charge on average lower interest rate relative to Banks (omitted variable), who charge

higher interest rates to clients. With reference to lending methodology, the MFIs with individual

lending are charging significantly high interest rate indicating that individual-based lenders tend to

be more profitable when their average interest rates are higher (Cull et al. (2008)). MFIs operating

EA & P, Latin American, ME & NA and SA are charging significantly low interest rates relative to

their African counterparts, who are charging significantly higher interest rates.

7.2.2 Lending Interest Rate and financial efficiency

From this regression, we have tried to find out the role of financially efficiency of MFIs in

determination of lending interest rate.

Log ILi = α + β1log IF

i + β2log FinEffi + β3log ROAi + β4log Outreachi + β5log Agei + β6 log womeni + µci

+ εi (4b)

Where Log ILi is the logarithm of lending interest rate, Log IF

i is logarithm of funding interest rate,

log FinEffi is logarithm of social efficiency, log ROAi is logarithm of return on asset, log Outreachi is

18

outreach indicator (loan size divided by GNI per capita in purchasing power parity); log Agei is the

logarithm of the number of years since the MFI has been operating and β5log womeni is the

logarithm of the percentage of female borrowers, for MFI i. The omitted variable categories are

the same as used for Equation 4a.

In Table 5, results in regression equation 1(ii) suggests that financial efficiency of MFIs has

significant positive impact on the interest rate unlike social efficiency. Even after controlling for

outreach, gender and ROA (in equation 2(ii), 3(ii) and 4(ii) respectively), the significant positive

relationship holds. Thus unlike social efficiency regression equations, outreach and gender do not

seem to impact interest rates when financial efficiency is concerned. Like social efficiency

regression equations, the variable ROA has a significant positive impact on interest rate. While,

similar to the social efficiency regressions, outreach (average loan size) has a significant inverse

relation with interest rate even after controlling for ROA in equation 4(ii) showing the fact that

smaller is the loan size, higher is the rate of interest. Similarly, the impact of gender on the lending

rate remains positive and significant. MFIs age has significant inverse relation with lending interest

rate which suggests that with the passage of time MFIs tend to charge lower interest rates.

Notwithstanding the dummy variables, out results stay the same as has been found for the social

efficiency regression equations.

From the above results, it can be concluded that funding rate of interest, financial and social

efficiency, ROA, outreach, gender and age all play significant role in determining lending interest

rate in microfinance (Cull et al. (2008) and Cotler P. (2010)).

6. Conclusion

This essay adds to the existing literature on the public policy issues in microfinance by

taking on for the first time the social as well as the financial efficiency as determinants of interest

rates in microfinance based on the data of 291 MFIs in 67 countries worldwide for four years

19

(2005-08). At the outset of this paper, we endeavored to take on a key issue in public policy

debate in microfinance: In general, what drives the interest rates in microfinance and in particular,

the impact of efficiency in microfinance on the interest rates. Towards this end, in line with the

double bottom line approach of microfinance, not only financial but also social efficiency has been

measured by employing non parametric framework.

In general the efficiency results suggest that globally the microfinance institutions are on

average more financially efficient rather than socially efficient. In particular, the results suggest

that on average with respect to status Banks are financially efficient while NGOs are socially

efficient, similarly MFIs with individual lending methodology are financially whereas with group

and village banking methodology are more socially efficient. While regulated MFIs are on average

more financially efficient and unregulated ones more socially efficient. Moreover, MFIs without

savings are financially and socially efficient whereas MFIs without nonfinancial services are

financially and with these services are socially efficient.

The issue of the determinants of interest rates in microfinance particularly the impact of

efficiency has been addressed by employing random-effect Tobit model. The results confirm that

social efficiency of MFIs has significant negative relationship with interest rate. However, after

controlling for loan size and gender it has positive but insignificant impact on interest rate which

depicts the fact as outreach increases, it causes the lending interest to rise. On the other hand,

financial efficiency of MFIs has significant positive relation with interest rate even after controlling

for outreach, gender and ROA. In line with the findings of Rosenberg et al. (2009) and Cotler P.

(2010), ROA has a significant positive impact on interest rate thus shows that increase in profits

leads to higher interest rates charges by the MFIs.

On the other hand, loan size has a significant inverse relation with interest showing the fact

that smaller is the loan size, higher is the rate of interest. The reason for high interest rate for

small loan size is high administrative costs which consist mainly of rent, utility charges, transport, 20

and depreciation of fixed assets and recovering small loans. Further, evidence has found that

gender has positive significant relation with interest rate even after controlling for loan size thus

confirms the fact that the MFIs which have predominantly women borrower, charge higher

interest rates to their clients because of small loan size.

21

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24

Table 1Inputs and Outputs in Efficiency Specifications

Variable Variable name Not. Definition Unit Papers using this input/ output

Input Total assets A Total of all net asset accounts ($)

Berger and Humphrey (1997);

Seiford and Zhu (1999); Luo

(2003) and Gutiérrez-Nieto et al.

(2009)

InputOperating

CostC

Expenses related to operations,

such as all personnel expenses,

rent and utilities, transportation,

office supplies, and depreciation

($)

Athanassopoulos (1997), Berger

and Humphrey (1997); Pastor

(1999) and Gutiérrez-Nieto et al.

(2007) and (2009)

InputNumber of

StaffE

The number of individuals who

are actively employed by the MFI. No.

Athanassopoulos (1997), Berger

and Humphrey (1997), Sherman

and Gold (1985); Seiford and Zhu

(1999); Luo (2003) and Gutiérrez-

Nieto et al. (2009)

Financial Efficiency

OutputGross loan

portfolioL

Outstanding principal balance of

all of the MFI’s outstanding loans ($)

Sherman and Gold (1985);

Athanassopoulos (1997); Berger

and Humphrey, 1997; Wheelock

and Wilson (1999) and Gutiérrez-

Nieto et al. (2007) and (2009)

Output Financial

revenue

R Revenue generated from the

gross loan portfolio and from

investments plus other operating

revenue

($) Pastor (1999) and Seiford; Zhu

(1999) and Gutiérrez-Nieto et al.

(2009)

Social Efficiency

Output

Number of

women

borrowers

WNumber of active borrowers who

are femaleNo.

Hashemi et al. (1996); Nieto et al.

(2009); Makame and Murinde

(2006) and Goetz and Gupta

(1996).

Output

Indicator of

benefit to the

poorest

P

Number of poor borrowers (See

the text for the variable

construction)

No.

Nieto et al. (2009)

25

21.38%

18.97%55.17%

4.483%

G IIG VB

Lending Methodology

48.11%51.89%

No Yes

Type(regulated)

51.2%48.8%

No Yes

Other Services

Figure 1: Pictorial analysis of Data (By Categories)

26

19.59%

6.186%

16.49%

36.77%

7.904%

13.06%

AFRICA EA & PFEE & CA LA & CM.E & NA S.A

MFIsRegions

8.247%

10.65%

29.21%

49.83%

2.062%

BANK CooperativeNBFI NGORURAL BANK

Status

52.58%47.42%

No Yes

Savings

Table 2

Variable Description and Summary Statistics

Inputs & Outputs Obs Definition Unit Mean Med. Min Max

Total asset (A) 1164

Average of current year (t) and previous year (t-1) assets. It includes all asset accounts net of all contra-asset accounts, such as the loan-loss allowance and accumulated depreciation. $(000s) 40100 7900 76 1120000

Operational cost (C) 1164

Expenses related to operations, such as all personnel expenses, rent and utilities, transportation, office supplies, and depreciation $(000s) 6777 1700 96 77300

Staff (E) 1164The number of individuals who are actively employed by the MFI. No. 620 103.5 9 24457

Outstanding Loan Portfolio (LP) 1164

The outstanding principal balance of all of an MFI’s outstanding loans $(000s) 30000 5900 94 837000

Financial Revenues 1164

Revenue generated from the gross loan portfolio (R) and from investments plus other operating revenue $(000s) 14200 2900 28525.34 540000

total women borrowers 1164 Number of active borrowers who are female No. 93045.93 83865 36 6500000

Organizational variables

Operational Self Sufficiency(OSS) 1164

Financial Revenue (Total)/ (Financial expense + Loan loss provision expense + Operating expense) (%) 129.75 116.37 0.9 10834

Average loan size 1164

Gross Loan Portfolio / Number of active borrowers 1154.71 436.5 20 49077

Loansize/GNIpc (ppp) 1164 Average loan size/ GNI per capita (pp) 0.28 0.12575 0.01 7.83

GNI per capita (current) 1164

Gross national income divided by the population. $ 1404 160 6070

MFI age 1164 The years since MFI has started operations No. 13.12 11 1 58

Borrowers 1164

The number of individuals who currently have an outstanding loan balance with the MFI or are responsible for repaying any portion of the Gross Loan Portfolio No. 110181 13775.5 119 6397635

Women borrowers 1164 Percentage of borrowers who are women % 65.58 64.84 3 100total women borrowers 1164 Number of active borrowers who are female No. 93045.93 83865 36 6500000

Lr (lending rate) 1164 Financial Revenue/avg. Loan Portfolio (%) 35 30.54 4 208No. of poor borrowers 1164 Number of poor borrowers No. 104058.8 12131.2 0 6260022fr (funding rate) 1164 Financial Expense/Total Debt (%) 14 9.03 0 1615

borrowers/Staff 1164 Number of Borrowers /Personnel No. 147.47 131 3 1209

cost/borrower 1164Operating Expense/ average no. of Active Borrowers ($) 173.17 101 1 4301

Source: Own calculation

27

0.2

.4.6

.81

avg.

finv

rste

& s

ocvr

ste

Bank Cooperative NBFI NGO Rural Bank

StatusAll Regions

mean of avgfinvrste mean of avgsocvrste

28

0.2

.4.6

.81

avg.

finv

rste

& s

ocvr

ste

Africa EA & PF EE & CA LA & C ME & NA SA

All Regions

mean of avgfinvrste mean of avgsocvrste

0.2

.4.6

.8av

g. fi

nvrs

te &

soc

vrst

e

No Yes

Type (regulated)All Regions

mean of avgfinvrste mean of avgsocvrste

29

0.2

.4.6

.81

avg.

finv

rste

& s

ocvr

ste

G I IG VB

Lending MethodologyAll Regions

mean of avgfinvrste mean of avgsocvrste

0.2

.4.6

.8av

g. fi

nvrs

te &

soc

vrst

e

No Yes

SavingsAll Regions

mean of avgfinvrste mean of avgsocvrste

0.2

.4.6

.8av

g. fi

nvrs

te &

soc

vrst

e

No Yes

Other ServicesAll Regions

mean of avgfinvrste mean of avgsocvrste

Fig.2: Comparison between average Financial Efficiency (ACE-LR) and Social Efficiency (ACE-WP)

30

Table 3 Average Financial Efficiency and Social Efficiency Scores (vrs)

Region Status Lending Methodology Regulated Savings Other services

Fin. Soc. Fin. Soc. Fin. Soc. Fin. Soc. Fin. Soc. Fin. Soc.

Africa 0.82 0.57 Bank 0.92 0.59 Group 0.89 0.70 No 0.87 0.65 No 0.88 0.65 No 0.89 0.60

EA & PF 0.95 0.76 Coop. 0.85 0.48 Indivi. 0.91 0.62 Yes 0.88 0.60 Yes 0.87 0.60 Yes 0.86 0.66

EE & CA 0.91 0.62 NBFI 0.86 0.59 Indi. & Group 0.86 0.59

LA & C 0.85 0.56 NGO 0.88 0.69 Village 0.83 0.70

ME & NA 0.94 0.73 R. Bank 0.86 0.46

SA 0.89 0.77Source: Own calculationsAverage scores are based on total of 1164 observations.

1

2

Table 4Pearson correlation coefficients

Lending rate

Funding rate

Financial Efficiency

Social Efficiency ROA Outreach Women Age

Lending rate 1

Funding rate

0.0613*0.0366 1

Financial efficiency

0.01210.6795

0.1270*0 1

Social efficiency

0.0583*0.0466

-0.00310.9165

0.22740 1

ROA0.3155*

0-0.02980.3099

0.12070

* 0.03830.1911 1

Outreach-0.3717*

00.032

0.2758-0.03120.2881

-0.6202*0

-0.1160*0.0001 1

Women0.3204*

0-0.03150.2831

-0.02130.4687

0.4051*0

0.0848*0.0038

-0.53730 1

Age-0.0985*0.0008

0.1774*0

-0.00840.7746

-0.01010.7314

-0.0689*0.0187

-0.01980.4998

0.0679*0.0204 1

*Refers to significant at 10%, 5% and 1% level of confidence

3

Table 5Random Effect Tobit Regressions (Dependant variable: Lending interest rate)

1 2 3 4

(i) (ii) (i) (ii) (i) (ii) (i) (ii)Social efficiency

Financial efficiency

-0.1515***(-6.79)

0.2131***(3.89)

-0.000(-0.31)

0.2029***(3.59)

0.01743(0.90)

0.2000***(3.51)

-0.1524***(-6.75)

0.2264***(4.11)

Funding Rate of Interest

0.01226**(2.05)

0.0101*(1.84)

0.0062(1.10)

0.0066(1.17)

0.00426(0.75)

0.0045(0.79)

0.0119**(2.15)

0.0109**(1.97)

Return on Asset 0.0329***(4.93)

0.0314***(4.70)

0.0329***(4.74)

0.0315***(4.58)

0.0329***(4.73)

0.0316***(4.57)

Loan size/GNI pc -0.2508***(-11.78)

-0.1726***(-9.39)

-0.2526***(-11.68)

-0.1736***(-9.33)

Gender 0.1136*** (4.40)

0.0927***(3.58)

0.1501***(5.52)

0.1487***(5.64)

0.1166*** (4.39)

0.0940***(3.59)

Age -0.0268(-1.04)

-0.0525**(-2.00)

-0.0916***(-3.35)

-0.0919***(-3.31)

-0.093***(-3.33)

-0.0917***(-3.29)

-0.0377(-1.45)

-0.0642**(-2.42)

Cooperative -0.3896***(-4.42)

-0.3768***(-3.99)

-0.4280***(-4.28)

-0.4158***(-4.13)

-0.4616***(-4.40)

-0.4537***(-4.30)

-0.3991***(-4.43)

-0.3859***(-4.00)

NBFI -0.1876**(-2.34)

-0.1574*(-1.83)

-0.0930(-1.03)

-0.0820(-0.90)

-0.0980(-1.03)

-0.0847(-0.88)

-0.1879**(-2.29)

-0.1570*(-1.79)

NGO -0.2137**(-2.39)

-0.1977**(-2.06)

0.0593(-0.59)

0.0570(-0.56)

0.0525(-0.50)

0.0414(-0.39)

-0.2052**(-2.24)

-0.1894*(-1.93)

Rural Banks -0.2566*(-1.66)

-0.1459(-0.88)

-0.1727(-0.98)

-0.1446(-0.82)

-0.1803(-0.98)

-0.1664(-0.90)

-0.2927*(-1.85)

-0.1781(-1.05)

Individual 0.9978*(1.67)

0.1105*(1.72)

0.0731(1.08)

0.0731(1.09)

0.0495(0.70)

0.0460(0.64)

0.1049*(1.71)

0.1153*(1.76)

Individual & Group -0.0398(-0.77)

0.0442(0.80)

-0.0243(-0.42)

-0.0140(-0.24)

-0.0372(-0.61)

-0.0309(-0.50)

0.0493(0.94)

0.5264(0.95)

Village banking -0.0256(-0.26)

-0.0135(-0.13)

-0.0209(-0.19)

-0.0031(-0.03)

-0.0231(-0.20)

-0.0035(-0.03)

-0.0169(-0.17)

-0.0041(-0.04)

Regulated -0.0394(-0.77)

-0.0819(-1.49)

-0.0716(-1.23)

-0.0824(-1.41)

-0.0833(-1.36)

-0.0894(-1.45)

-0.0373(-0.71)

-0.0807(-1.44)

Savings -0.01146(-0.23)

-0.0092(-0.18)

-0.01862(-0.33)

-0.0132(-0.24)

-0.0270(-0.47)

-0.0221(-0.38)

-0.0098(-0.20)

-0.0077(-0.15)

Other Services -0.01704(-0.45)

-0.0171(-0.42)

0.0007(0.02)

0.0058(0.13)

0.0095(0.21)

0.0174(0.38)

-0.0148(-0.38)

-0.0143(-0.34)

EA & P -0.2667***(-2.73)

-0.2498**(-2.39)

0.0020(0.02)

-0.0329(-0.30)

0.0424(0.37)

0.0171(0.15)

-0.2581***(-2.58)

-0.2441**(-2.28)

EE & CA 0.0334(0.47)

-0.0622(-0.84)

-0.0921(-1.16)

-0.1172(-1.46)

-0.1362(-1.64)

-0.1547*(-1.84)

0.0268(0.37)

-0.0721(-0.94)

LA & C -0.1153*(-1.89)

-0.0839(-1.29)

0.0219(0.32)

0.0092(0.13)

0.0142(0.20)

0.0009(0.01)

-0.1103*(-1.77)

-0.0795(-1.20)

ME & NA -0.2966***(-3.41)

-0.3109***(-3.32)

-0.1429(-1.47)

-0.1749*(-1.78)

-0.1607(-1.57)

-0.1860*(-1.80)

-0.2922***(-3.29)

-0.3086***(-3.24)

SA -0.5698***(-7.99)

-0.5681***(-7.41)

-0.4143***(-5.21)

-0.4359***(-5.44)

-0.3997***(-4.78)

-0.4146***(-4.92)

-0.5867***(-8.05)

-0.5854***(-7.50)

Constant -1.1587***(-7.66)

-0.8272***(-5.40)

-0.5271***(-3.33)

-0.4927***(-3.12)

-0.5657***(-3.43)

-0.5588***(-3.40)

-1.2587***(-8.23)

-0.9158***(-5.92)

No. of Observation 1164 1164 1164 1164 1164 1164 1164 1164No. of Groups 291 291 291 291 291 291 291 291Log likelihood -75.63 -90.65 -139.278 -132.95 -154.34 -148.63 -87.73 -101.68

Wald Chi2 356.73 297.01 173.73 185.66 132.50 143.00 321.20 266.41prob>Chi2 0 0 0 0 0 0 0 0

*significant at 10%; ** significant at 5%; *** significant at 1%; z-values in parenthesesSource: Based on author’s own calculation

4

5