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,
1
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 costcutting 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
Nimal A. Fernando (2006), “Understanding and Dealing with High Interest Rates on Microcredit, A Note to Policy
Makers in the Asia and Pacific Region”
1
2
(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 Market 2
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
3
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
4
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 nonNGOs. 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
6
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 LRACE3 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 indicator
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
5
8
institution lends to the poorest. In order to find the objective of reaching poor (pi) we deduct Kmin (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
4.
(3)
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
9
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)
Efficiency Analysis
5.
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
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.
6
10
efficiency (Scale). However in the Table 3, only the average efficiency scores for specification LRACE (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)
(Hardy et al., 2003; Marulanda and Otero, 2005)
9
(Hartarska (2005, 2009)) and (Mersland and Strom (2009))
10
See Barry et al., (2009)
8
12
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)
Nimal A. Fernando (2006), “Understanding and Dealing with High Interest Rates on Microcredit, A Note to Policy
Makers in the Asia and Pacific Region”
11
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
14
actually the most 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 2008
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))
13
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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 IFi + 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 IFi 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
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)
15
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
17
interest 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 IFi + 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 IFi 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 1
Inputs and Outputs in Efficiency Specifications
Variable
Variable name
Not.
Definition
Unit
Papers using this input/ output
Berger and Humphrey (1997);
Input
Total assets
A
Total of all net asset accounts
($)
Seiford and Zhu (1999); Luo
(2003) and Gutiérrez-Nieto et al.
(2009)
Expenses related to operations,
Input
Operating Cost
C
such as all personnel expenses,
rent and utilities, transportation,
Athanassopoulos (1997), Berger
($)
office supplies, and depreciation
and Humphrey (1997); Pastor
(1999) and Gutiérrez-Nieto et al.
(2007) and (2009)
Athanassopoulos (1997), Berger
Input
Number of
Staff
E
The number of individuals who are
actively employed by the MFI.
and Humphrey (1997), Sherman
No.
and Gold (1985); Seiford and Zhu
(1999); Luo (2003) and GutiérrezNieto et al. (2009)
Financial Efficiency
Sherman and Gold (1985);
Output
Gross loan
portfolio
L
Outstanding principal balance of
all of the MFI’s outstanding loans
Athanassopoulos (1997); Berger
($)
and Humphrey, 1997; Wheelock
and Wilson (1999) and GutiérrezNieto et al. (2007) and (2009)
Revenue generated from the gross
Output
Financial
revenue
R
loan portfolio and from
investments plus other operating
Pastor (1999) and Seiford; Zhu
($)
(1999) and Gutiérrez-Nieto et al.
(2009)
revenue
Social Efficiency
Hashemi et al. (1996); Nieto et al.
Number of
Output
women
W
borrowers
benefit to the
poorest
are female
No.
(2009); Makame and Murinde
(2006) and Goetz and Gupta
(1996).
Indicator of
Output
Number of active borrowers who
Number of poor borrowers (See
P
the text for the variable
construction)
25
Nieto et al. (2009)
No.
Lending Methodology
Regions
MFIs
4.483%
13.06%
21.38%
19.59%
7.904%
6.186%
18.97%
55.17%
16.49%
36.77%
AFRICA
EE & CA
M.E & NA
EA & PF
LA & C
S.A
G
IG
I
VB
Type(regulated)
Status
2.062%
8.247%
10.65%
48.11%
49.83%
51.89%
29.21%
BANK
NBFI
RURAL BANK
Cooperative
NGO
No
Savings
Other Services
47.42%
48.8%
51.2%
52.58%
No
Yes
No
Yes
Figure 1: Pictorial analysis of Data (By Categories)
26
Yes
Table 2
Variable Description and Summary Statistics
Inputs &
Outputs
Obs
Total asset (A)
1164
Operational
cost (C)
1164
Staff (E)
Outstanding
Loan Portfolio
(LP)
Financial
Revenues
total women
borrowers
1164
Definition
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 loanloss allowance and accumulated
depreciation.
Expenses related to operations, such as all
personnel expenses, rent and utilities,
transportation, office supplies, and
depreciation
The number of individuals who are actively
employed by the MFI.
Unit
Mean
Med.
Min
Max
$(000s)
40100
7900
76
1120000
$(000s)
6777
1700
96
77300
No.
620
103.5
9
24457
$(000s)
30000
5900
94
837000
$(000s)
14200
2900
28525.34
540000
1164
The outstanding principal balance of all of an
MFI’s outstanding loans
Revenue generated from the gross loan
portfolio (R) and from investments plus
other operating revenue
1164
Number of active borrowers who are female
No.
93045.93
83865
36
6500000
1164
(%)
129.75
116.37
0.9
10834
1164
Financial Revenue (Total)/ (Financial expense
+ Loan loss provision expense + Operating
expense)
Gross Loan Portfolio / Number of active
borrowers
1154.71
436.5
20
49077
1164
Average loan size/ GNI per capita (pp)
0.28
0.12575
0.01
7.83
$
No.
1404
13.12
11
160
1
6070
58
No.
110181
13775.5
119
6397635
%
65.58
64.84
3
100
1164
Organizational
variables
Operational Self
Sufficiency(OSS)
Average loan
size
Loansize/GNIpc
(ppp)
GNI per capita
(current)
MFI age
Borrowers
Women
borrowers
total women
borrowers
1164
Gross national income divided by the
population.
The years since MFI has started operations
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
1164
Percentage of borrowers who are women
1164
Number of active borrowers who are female
No.
93045.93
83865
36
6500000
Lr (lending rate)
No. of poor
borrowers
fr (funding
rate)
1164
Financial Revenue/avg. Loan Portfolio
(%)
35
30.54
4
208
1164
Number of poor borrowers
No.
104058.8
12131.2
0
6260022
1164
Financial Expense/Total Debt
(%)
14
9.03
0
1615
borrowers/Staff
1164
No.
147.47
131
3
1209
($)
173.17
101
1
4301
1164
1164
Number of Borrowers /Personnel
Operating Expense/ average no. of Active
cost/borrower
1164 Borrowers
Source: Own calculation
27
All Regions
All Regions
.8
.6
.4
.2
0
.2
.4
.6
.8
avg. finvrste & socvrste
1
1
Status
0
Bank
Africa
EA & PF
EE & CA
LA & C
mean of avgfinvrste
ME & NA
SA
Cooperative
NBFI
mean of avgfinvrste
NGO
Rural Bank
mean of avgsocvrste
mean of avgsocvrste
All Regions
Type (regulated)
.6
.4
.2
0
0
.2
.4
.6
.8
avg. finvrste & socvrste
.8
1
All Regions
Lending Methodology
G
I
IG
mean of avgfinvrste
No
VB
Yes
mean of avgfinvrste
mean of avgsocvrste
All Regions
Savings
Other Services
.6
.4
.2
0
0
.2
.4
.6
avg. finvrste & socvrste
.8
.8
All Regions
mean of avgsocvrste
No
mean of avgfinvrste
No
Yes
mean of avgfinvrste
mean of avgsocvrste
Yes
mean of avgsocvrste
Fig.2: Comparison between average Financial Efficiency (ACE-LR) and Social Efficiency (ACE-WP)
28
Table 3
Average Financial Efficiency and Social Efficiency Scores (vrs)
Region
Status
Fin.
Soc.
Africa
0.82
0.57
EA & PF
0.95
EE & CA
Lending Methodology
Fin.
Soc.
Fin.
Soc.
Bank
0.92
0.59
Group
0.89
0.70
0.76
Coop.
0.85
0.48
Indivi.
0.91
0.62
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
0.77
Source: Own calculations
Average scores are based on total of 1164 observations.
1
Regulated
Savings
Fin.
Soc.
No
0.87
0.65
Yes
0.88
0.60
Other services
Fin.
Soc.
No
0.88
0.65
Yes
0.87
0.60
Fin.
Soc.
No
0.89
0.60
Yes
0.86
0.66
2
Table 4
Pearson correlation coefficients
Lending
rate
Funding
rate
Financial
efficiency
Social
efficiency
ROA
Outreach
Women
Lending
rate
Funding
rate
1
0.0613*
0.0366
0.0121
0.6795
0.0583*
0.0466
0.3155*
0
-0.3717*
0
0.3204*
0
-0.0985*
0.0008
1
0.1270*
0
-0.0031
0.9165
-0.0298
0.3099
0.032
0.2758
-0.0315
0.2831
0.1774*
0
Financial
Social
Efficiency Efficiency
1
0.2274
0
0.1207
0
-0.0312
0.2881
-0.0213
0.4687
-0.0084
0.7746
1
* 0.0383
0.1911
-0.6202*
0
0.4051*
0
-0.0101
0.7314
ROA
Outreach
Women
Age
1
-0.1160*
0.0001
0.0848*
0.0038
-0.0689*
0.0187
1
-0.5373
0
-0.0198
0.4998
1
0.0679*
0.0204
1
Age
*Refers to significant at 10%, 5% and 1% level of confidence
3
Table 5
Random Effect Tobit Regressions (Dependant variable: Lending interest rate)
1
(i)
Social efficiency
2
(ii)
-0.1515***
(-6.79)
Financial efficiency
(i)
(ii)
-0.000
(-0.31)
0.2131***
(3.89)
4
3
(i)
(ii)
0.01743
(0.90)
0.2029***
(3.59)
(i)
(ii)
-0.1524***
(-6.75)
0.2000***
(3.51)
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)
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)
-0.2508***
(-11.78)
0.1136***
(4.40)
-0.0268
(-1.04)
-0.3896***
(-4.42)
-0.1726***
(-9.39)
0.0927***
(3.58)
-0.0525**
(-2.00)
-0.3768***
(-3.99)
0.1501***
(5.52)
-0.0916***
(-3.35)
-0.4280***
(-4.28)
0.1487***
(5.64)
-0.0919***
(-3.31)
-0.4158***
(-4.13)
-0.093***
(-3.33)
-0.4616***
(-4.40)
-0.1876**
(-2.34)
-0.1574*
(-1.83)
-0.0930
(-1.03)
-0.0820
(-0.90)
-0.2137**
(-2.39)
-0.2566*
(-1.66)
0.9978*
(1.67)
-0.0398
(-0.77)
-0.0256
(-0.26)
-0.1977**
(-2.06)
-0.1459
(-0.88)
0.1105*
(1.72)
0.0442
(0.80)
-0.0135
(-0.13)
0.0593
(-0.59)
-0.1727
(-0.98)
0.0731
(1.08)
-0.0243
(-0.42)
-0.0209
(-0.19)
-0.0394
(-0.77)
-0.0819
(-1.49)
-0.01146
(-0.23)
-0.01704
(-0.45)
-0.2667***
(-2.73)
EE & CA
0.2264***
(4.11)
0.0119**
(2.15)
0.0109**
(1.97)
-0.0917***
(-3.29)
-0.4537***
(-4.30)
-0.2526***
(-11.68)
0.1166***
(4.39)
-0.0377
(-1.45)
-0.3991***
(-4.43)
-0.1736***
(-9.33)
0.0940***
(3.59)
-0.0642**
(-2.42)
-0.3859***
(-4.00)
-0.0980
(-1.03)
-0.0847
(-0.88)
-0.1879**
(-2.29)
0.0570
(-0.56)
-0.1446
(-0.82)
0.0731
(1.09)
-0.0140
(-0.24)
-0.0031
(-0.03)
0.0525
(-0.50)
-0.1803
(-0.98)
0.0495
(0.70)
-0.0372
(-0.61)
-0.0231
(-0.20)
0.0414
(-0.39)
-0.1664
(-0.90)
0.0460
(0.64)
-0.0309
(-0.50)
-0.0035
(-0.03)
-0.2052**
(-2.24)
-0.2927*
(-1.85)
0.1049*
(1.71)
0.0493
(0.94)
-0.0169
(-0.17)
-0.1570*
(-1.79)
-0.1894*
(-1.93)
-0.0716
(-1.23)
-0.0824
(-1.41)
-0.0833
(-1.36)
-0.0894
(-1.45)
-0.0373
(-0.71)
-0.0092
(-0.18)
-0.0171
(-0.42)
-0.2498**
(-2.39)
-0.01862
(-0.33)
0.0007
(0.02)
0.0020
(0.02)
-0.0132
(-0.24)
0.0058
(0.13)
-0.0329
(-0.30)
-0.0270
(-0.47)
0.0095
(0.21)
0.0424
(0.37)
-0.0221
(-0.38)
0.0174
(0.38)
0.0171
(0.15)
-0.0098
(-0.20)
-0.0148
(-0.38)
-0.2581***
(-2.58)
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)
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)
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)
-0.5698***
(-7.99)
-1.1587***
(-7.66)
1164
291
-75.63
356.73
0
-0.5681***
(-7.41)
-0.8272***
(-5.40)
1164
291
-90.65
297.01
0
-0.4143***
(-5.21)
-0.5271***
(-3.33)
1164
291
-139.278
173.73
0
-0.4359***
(-5.44)
-0.4927***
(-3.12)
1164
291
-132.95
185.66
0
-0.3997***
(-4.78)
-0.5657***
(-3.43)
1164
291
-154.34
132.50
0
-0.4146***
(-4.92)
-0.5588***
(-3.40)
1164
291
-148.63
143.00
0
-0.5867***
(-8.05)
-1.2587***
(-8.23)
1164
291
-87.73
321.20
0
Loan size/GNI pc
Gender
Age
Cooperative
NBFI
NGO
Rural Banks
Individual
Individual & Group
Village banking
Regulated
Savings
Other Services
EA & P
SA
Constant
No. of Observation
No. of Groups
Log likelihood
Wald Chi2
prob>Chi2
*significant at 10%; ** significant at 5%; *** significant at 1%; z-values in parentheses
Source: Based on author’s own calculation
4
-0.1781
(-1.05)
0.1153*
(1.76)
0.5264
(0.95)
-0.0041
(-0.04)
-0.0807
(-1.44)
-0.0077
(-0.15)
-0.0143
(-0.34)
-0.2441**
(-2.28)
-0.0721
(-0.94)
-0.0795
(-1.20)
-0.3086***
(-3.24)
-0.5854***
(-7.50)
-0.9158***
(-5.92)
1164
291
-101.68
266.41
0
5