Empirical Findings on Cognitive Dissonance Around
Microfinance Outreach and Sustainability
Keywords:
Outreach; sustainability; efficiency
Abstract
This paper undertakes empirical assessment of microfinance commercialization factors to
probe the cognitive dissonance surrounding microfinance outreach and sustainability.
Specifically the paper analyzes a balanced panel of 198 observations consisting of 33
MFIs over a period of six years from 2000 to 2005. The sample of the MFIs has been
constructed from those organizations that report their information to the Mix MarketTM.
To robust our results a number of theoretical approaches defining outreach depth are
tested. It has been observed the commercialization factors do not significantly explain the
depth or breadth of outreach and age having a positive relationship with outreach depth.
It has also been seen that efficient MFIs are the ones that have greater potential of
reaching the poorest.
JEL Classification:
C23; G21; G28; I39
Correspondence to: A H Makame, Birmingham Business School, University of
Birmingham, University House, Edgbaston, Birmingham B15 2TT. Tel + 44 (0) 121 415
8021, email [email protected]
1
Empirical Findings on Cognitive Dissonance Around
Microfinance Outreach and Sustainability
1
1.1
Introduction
Aims and Motivation:
The comprehensive literature survey paper suggests that there are numerous research
agendas that need to be pursued. It has been argued that commercializing microfinance is
likely to result into a mission drift; Christen (2001) found that it was not the case and that
sustainability does not necessarily compromise outreach. Latin American MFIs led the
globe to commercialization, one of the very few serious innovations in microfinance
outside Bangladesh. Empirical and theoretical studies have reported varying findings.
This paper seeks to probe this matter with regards to the East African Region.
The cognitive dissonance surrounding subsidizing microfinance institutions to promote
outreach at the illusion of trading of sustainability has been manifested in the literature of
the area. The welfarists versus the institutionalists debated for a while; though the tug of
war is yet to be over, members from each camp have developed the tendency of moving
to the neutral center ground (see Woller, 2002; Morduch, 2005). We have the anxiety to
find out whether our study and methodology will conform to theory in literature as well
as findings from other studies. Our paper, to the extent of our knowledge will be the first
one to probe this matter in the eastern part of Africa.
1.2
Regional Background
East Africa is a region consisting predominantly of Tanzania, Kenya and Uganda;
Rwanda and Burundi are also said to be constituents of the East African block. Tanzania
is the largest in terms of area and population1, Uganda follows Kenya whilst Rwanda and
1
See Tables 1 and 2 for real and financial indicators and population and poverty indicators respectively for
the countries covered by this study.
2
Burundi are the tiniest. Kenya is the best performer economically as suggested by its
GDP compared to other nations in East Africa; Rwanda has recorded the least
performance. Tanzania and Kenya have access to the Indian Ocean, whereas the
remaining three countries are landlocked.
The GDP per capita of the countries in question is far below that of the global poverty
level, which should be approximately $7202. Annual inflation according to consumer
prices suggests that it is unfavorable to invest domestically in bank deposits as the
countries’ deposit rates have been reported to be lower than the inflation rate in both
2000 and 2005. In a situation where inflation exceeds deposit interest rates, the economy
is said to have a negative effective return. However, the economies of the region are seen
to have generally improved over the years from 2000 through to 2005. This is indicated
by decrement in inflation and interest rate spread (It has is argued that narrower spreads
are a good indication of economic efficiency) albeit the deposit rates also went down by a
range eleven and thirty seven percent between the 6 year period ending 2005. This
affected the demand for deposit products as the rate of inflation was in many cases higher
than the deposit rate.
Insert table 1 about here
The region also reported an encouraging GDP growth rate as depicted by individual
countries reports in table 1. In 2000 the country with highest GDP growth rate was
Rwanda and in 2005 it was Tanzania, Kenya is the country with lowest in both year ends.
The economies of these countries are collectively referred to as the East African
Community, and have many characteristics in common. Perhaps the most common factor
is the population distribution in these countries, which is above seventy five percent
rural3 in all the countries under study. Likewise the female population is almost fifty
percent across the countries in the region and has remained more or less at the same level.
Despite the fact that the economies countries depend mainly on agricultural production to
2
3
This is referred to as the synthetic GDP per capita and formerly discussed in section four.
See Table 2 for population and poverty indicators.
3
sustain themselves, and that agricultural producing activities mainly take place in the
rural areas; the non-urban areas are less developed in terms of infrastructure and other
crucial facilities, with their population suffering from extreme poverty. Duursma (2004)
estimates that poverty affects eighty to ninety percent of the rural poor in East Africa: this
assertion translates that more than sixty percent of the East African population lives in
poverty prior to adding the proportion of the urban poor.
Kenya maintained the same rate of rural population growth in 2005 as it was in 2000,
Rwanda and Tanzania had their rural population growth rates for the same period, whilst
Uganda and Burundi experienced the contrary.
Insert table 2 about here
The region is a good representation of Least Developed Countries (LDCs) as the GDP per
capita is very low, poor infrastructure, high mortality rates of children under 5 (consult
WDI September 2006 for comparison) and all other necessary facilities being insufficient
as opposed to the developed nations. In 2004 the proportions of branches per 100,000
people were 1.38, 0.58 and 0.53 for Kenya, Tanzania and Uganda respectively; with
4.7% and 7% of people operating a deposit account in Uganda and Kenya in that order.
The financial sector development in East Africa is still at an early stage, with many banks
operating in the urban areas (Duursma 2004; Hussein and Makame, 2007a): the regional
financial sector policies should be looked into so as to develop as its development will
contribute to poverty reduction as suggested by Green and et al (2006); it is with that
motivation that governments and other stakeholders have undertaken deliberate initiatives
to attain financial sector development.
1.3
Microfinance Issues: A Brief Overview
Although microfinance had been in existent for as long as human civilization has been
around, it has only recently being used as a policy tool in a serious manner. Human
beings have been continually struggling against nature for survival at various levels from
individuals, to households and to the wider communities; ‘informal’ (as we call it)
4
finance had been in existent since those old days. Several pieces of literature endeavor to
illustrate the ways rural finance (which we nowadays classify it as part of microfinance)
and other forms of offering credit developed4.
Microfinance gained interest from academics, policy makers and other stake holders soon
after the unprecedented achievement of providing those whom were known by then as
‘non-bankables’ with credit which recorded repayment rates that even exceeded the ones
prevailing in the conventional banking systems. This change of thinking was brought to
light by Grameen Bank initiated by its founder, Muhammad Yunus the nobel laureate.
Grameen replications were made all over the world; others went a bit further by
introducing innovations in microlending, and became row models in their own rights5.
Initially preached as the poverty alleviation tool and promoted on welfarist grounds,
microfinance operations received hefty subsidies from various sources (see our earlier
chapter) that were later criticized as perpetuators of inefficiency and that financially
sustainable microfinance ought to be the target for long term sustainability of the
undertakings (Morduch 1999): this is the institutionalist view. It has been argued in our
earlier chapter and indeed other articles that these views are extreme points and that a
consensus is necessary to formulate a hybrid of the two.
Microfinance has been used to target poor women so as to enable them improve their
livelihoods; it has also been used as a policy for creating enabling environment for the
financially excluded populations to engage in self employment. Initially known as
microcredit as it was advancing credit to its clients, it grew to microfinance, where it
offers credit plus services (Aghion and Morduch 2005). These services include savings,
microinsurance, housing finance, education loans and other services including health
advice and capacity building. Perhaps the most stunning development in microfinance is
the raising of capital by MFIs in the capital markets, pioneered by Bancosol and BRI,
4
Adams and et al (1984) have covered a lot of this information and their work is a diamond mine of this
information.
5
A good example of this is the Bank Rakyat Indonesia (BRI) and Bancosol Bolivia which are referred to as
the standards to follow by the Tanzanian Microfinance Regulations, for a lengthy discussion see (Hussein
and Makame, 2007b)
5
others are following the suite; in Kenya, Faulu has its securities trading in the Nairobi
Stock Exchange.
Despite the fact that literature suggests that microfinance contributes towards alleviating
poverty, there is a wide literature in the area which argues that microfinance does not
reach the poorest of the poor (See Woller, 2002; Montgomery and Weiss, 2005; Hashemi
and Rosenberg, 2006; Fraser and Kazi, 2004). This situation has been explained by
Hashemi and Rosenberg (2006), who argue that for self sustainability, MFIs cannot be in
a position of serving the ultra poor: but the fundamental target remains largely
unattained; as the ultra poor are the targeted clientele in a welfarist approach. Have
microfinance policy failed to deliver the freedom from poverty dream? Is it not possible
to target the poorest of the poor using microfinance? Is it that there is a mission drift as
much as MFIs would appreciate depth of outreach, they would equally benefit from
continuity (sustainability, perpetual succession) and are the poor an extremely risky
portfolio to maintain? This brings about the cognitive dissonance surrounding
microfinance outreach and sustainability on one hand and welfare on the other. It is an
important policy question that has been in the frontlines of microfinance literature for
over a considerable time now.
This study endeavors to evaluate how outreach and sustainability is explained by
commercialization factors using empirical data structured as panel for the five East
African countries. We shall consider all the countries as one due to the fact that they are
members of the East African Community and possess similar characteristics as presented
in the regional background above. We seek to present our empirical findings so as to
contribute to the existing literature and provide platform for future research; it is our
anticipation that the chapter may bring about important policy considerations.
In what follows, this paper will be presented in 4 sections. Section 2 teases out
microfinance regulation, competition and commercialization. The third section discusses
on the data and methodological approach used in the study which itself covers the
theoretical framework, the data and estimation and testing procedures while section four
6
explores the results; section five offers conclusions policy recommendations and areas
of future research.
2
Microfinance Regulations, Competition and Commercialization
Christen (2001) argues that there are three key elements to microfinance
commercialization namely (i) strong financial performance, (ii) presence of competition
between MFIs; which will undoubtedly result into benefits to the users of their products
and (iii) attaining financial sustainability; presence of regulative practice indicates
financial sustainability as regulators tend to register sound MFIs with potentials of long
term financial sustainability. Christen suggests that only competitive MFIs will have long
term sustainability; and that in commercializing microfinance a win win scenario of
widening outreach and attaining sustainability will be attained. Christen (2001) is
effectively suggesting that there will be no mission drift between outreach and
sustainability; and that microfinance will be have two faces6, synonymous to Janus (see
figure 1 for illustration).
Figure 1:
Microfinance as the double faced portrait of The Greek God Janus
Outreach
Sustainability
 Poverty alleviation
 Profitable undertaking
 Reaches the masses even
 Minimizes costs and
at geographically remote
maximizes efficiency.
areas. (Financial Inclusion)
(Financial and long term
sustainable.
 Welfarist view
 Institutionalist
Microfinance brings together the two camps and attains objectives of both
Designed by Author: Obtained Janus Portrait from Google
Microcredit lending rates have been reported to be very high7, CGAP (2001) is among
numerous articles raising concerns that the poor may seem to be exploited by the very
institutions that were designed to assist their exit from the poverty trap. Where some
recommend that ceilings should be imposed so as to protect exposure of the vulnerable;
6
Churchill (2006) suggests a similar scenario to micro insurance, which is a subset of microfinance.
Makame, Murinde and Mullineux (2006) arrive at similar conclusions of a double faced mission. Isern and
Porteous (2006) and Prahalad (2005) suggest the dual objectives of microfinance can be attained.
7
for constructive discussion, see Fernando (2006)
7
others are actually suggesting that ceilings and any other form of intervention (including)
subsidizing should be abolished so as to allow the market forces to work and bring about
efficiency8. This is a topical debate which has divided the opinions of the microfinance
stakeholders, as to which is the best way forward.
Porteous (2006) has suggested that competition follows market development phases, and
utilized four market phases and four market characteristics to form a matrix (see figure 2)
that illustrates the nature of competition. He suggests that increased competition can
lower interest rates if some conditions are fulfilled, such as transparent and comparable
pricing across, which can be easily understood by the clients. Consumers financial
literacy ought to be promoted so as to enable them make wise and informed decisions.
The environment should simplify collection and assessment of credible market level
information and more importantly establishment of credible credit bureaus, this is also in
accord with earlier research see (Aghion and Morduch, 2004; Dellien and Schreiner,
2005) among others to get the tip of an iceberg.
The crux of the matter is, no matter what information is being provided, for and against
commercialization, the rates of interest being charged to microcredit borrowers, is in
many cases very high9. Navajas and et al (2003) are in a view that increased competition
may disrupt outreach, reinforcing the old belief of observing mission drift as a result of
commercialization. Olivares-Polanco (2005) Finds evidence of mission drift, a number of
other studies not mentioned here also are in view of the same.
Woller (2002), a staunch Welfarist, suggests that good practices should be implemented
in microfinance without risking alienating the poor; the commercialization warning sent
out by Woller is the likelihood of experiencing mission drift. Likewise, Morduch (2005),
8
Campion (2002) provides insights into the challenges of microfinance commercialization. Interesting
discussions can are also raised by (Charitonenkoand de Silva,2002; Charitonenko, Rahman, 2002 and
Charitonenko and et al, 2004)
9
Hashemi and Rosenberg (2006) are in an opinion that microfinance excludes the poor. The poor can be
excluded either voluntarily, by self-group formation as in the peer screening or in the MFI lending policy,
as it is crucial for lenders to avoid any potential bad debts. Self elimination is when a potential borrower
decides not to borrow so as to avoid financial distress that will result from exorbitant interest rates. Jackson
and Islam (2005) point out that MFIs charging less than 30% on lending rates are not likely to have long
term sustainability.
8
a known institutionalist proposes the usage of ‘smart subsidies’ in attaining the dual goals
of outreach (welfarism) and sustainability (institutionalism). Earlier chapters of our work
did point out that the advocates of the polar extreme schools of thought will converge to
the center; with concessions being made by each camp to incorporate advantages of both
approaches whilst discarding the disadvantages.
Figure 2: Proteous (2006) Characterization of Competition Through Market Development phases
Market
Market Phases
Characteristics
Pioneer
Take off
Consolidation
Mature
May be slow
Rapid
Positive but
Steady natural
Growth in
slowing
growth
Volume
One or a few
Increases rapidly
Reduces from
Depends on
Number of
peak because of
characteristics of
firms
consolidation
products and
markets
Concentrated
Fragmented
Concentrating,
Market leaders
Market
although market
Clear market
dominate
Structure
leader may emerge leader emerges
Little Competition
Product
Price
Branding
Arenas of
except as to
Characteristics and
(including
Competition
location of
service levels
pricing)
distribution points
When the welfarists and institutionalists had not converged, it was as though
Microfinance Janus (figure1) had his two faces facing each other in a big fight! However,
there is growth in literature advocating a moderate standard, where both good practices
and welfare should be utilized10. Gibbons and Meehan (2002) suggest that innovative
financing is essential in meeting client demands, competition is likely to contribute to
customer orientation; and appropriate regulation should be in place. Hardy and et al
(2003) also call for appropriate regulatory framework; Marulanda and Otero (2005) also
call for appropriate regulatory framework involving scaling up and scaling down as
appropriate and donor support for the excluded poor.
10
Matin and Hulme (2003) suggest that safety net programmes should used to target the poor, the same
proposal is given by Hashemi and Rosenberg (2006). Pretes M (2002) views grant based microfinance
reaches the poor, whilst Barua and Sulaiman propose MFIs to target the poor so as to encounter potential
exclusion.
9
Microfinance regulation is not an overnight policy and should take considerable time so
as to ensure objectives are not violated, see (Rubambey, 2005; Charitonenkoand de Silva,
2002; Charitonenko and Rahman, 2002 and Charitonenko and et al, 2004). The
proposition that regulated MFIs will become highly competitive, market oriented and
client led is not supported by the reporting of Galardo and et al (2005), they suggest the
two to be independent events. Jackson and Islam (2005) suggest that unregulated MFIs
can be very competitive and focused and that regulation should be done on a voluntary
basis; without the MFIs being pressurized by the authorities as regulation is not a
prerequisite for them to meet their goals. Jackson and Islam (2005) outline different ways
in which MFIs can be regulated.
Careful observations of regulatory practices by Kirkpatrick and Maimbo (2002) revealed
that entry regulations are in place, in most cases similar to the financial institution
regulations of a place in question. They noted that exit regulations are not in existence
and recommended the same to policy organs.
Since its inception, there has always been a cloud of skepticism around microfinance.
There was a serious hangover from the era of the failed subsidized credit to rural peasants
and farmers that the poor are not bankable; and in the conventional banking stance of
denying credit to clients with no collateral. Immediately after its inception, reported
success on microcredit was celebrated as a breakthrough policy towards poverty
alleviation. Others viewed it as a profitable way of serving the poor, whereas others
viewed it as an appropriate distribution tool of income in the form of subsidized credit.
As a result, impact assessment studies were carried out in a large scale, especially in the
90’s. The results of impact assessment studies were sometimes controversial, whereas
others concluded that microfinance has a positive impact on poverty alleviation, others
found the contrary. Later studies however concluded that there is a positive contribution
of microfinance on poverty alleviation.
There are wide number of studies which suggest that, although microfinance is having a
positive contribution towards eradication of poverty; the ones who benefit from it are the
poor elite! This is due to the very fact that the ultra poor need extra support which
microfinance cannot provide under its current banner. The questions of having MFIs on
10
sustainable operations and existence were raised soon after the issues of impact
assessment. These suggested the idea of having good practices whilst implementing
microfinance so as to have a sustainable existence.
The relevance of injecting subsidies in the operations of microfinance was brought to
light, where the institutionalists totally opposed having subsidies so as to be in line with
the so called ‘good practices’ and the welfarists supported subsidies as a measure to
improve social welfare. Eventually, it may be right to argue that there is a consensus of
accommodating smart subsidies, as MFIs have a two faced mission11, and it would almost
be impractical to attain both without a form of intervention which is subsidies.
Good practices include transparency in operations and reporting; it means less use of
subsidies with an ultimate goal of zero tolerance stance on subsidies: it requires MFIs to
operate efficiently at minimum costs and also adhere to the regulatory requirements set
by the microfinance regulator in the economy. A proper application and adoption of good
practices will result to sustainable operations which require no subsidy dependence; it
will result into the success of mining at the bottom of the pyramid.
Attaining subsidy-free sustainable existence by MFIs will certainly require the
employment of policies that will minimize the non performing loans; this will obviously
mean that the credit policies will have to be less liberal with stricter follow-up
mechanisms for unpaid/un-serviced loan portfolios. The ultra poor will exclude
themselves voluntarily; get excluded by the system or both due to obvious reasons
explained in earlier sections of this paper, implying that a trade off has to be made
between financial sustainability or deep outreach. Where deep outreach in this scenario
and for the remainder of this chapter means accessing the grass roots in the remote and
rural areas including the poorest of the poor and bringing them into the financial system
by offering them financial services. The tradeoff pointed out here is the so called
‘mission drift’.
11
See figure 1
11
The study conducts an analysis that aims to probe the old belief of having mission drift
in East Africa as a result of microfinance commercialization. We present data and
arguments in the process of our analysis and use the panel data technique in running
regressions. Likewise cross section regressions (though without time series, and therefore
not data rich as panel) are presented for the sake of comparison and comments.
According to Makame and et al (2006) subsidies are essential for microfinance goals of
financial
inclusion,
outreach
and
sustainability
without
compromising
‘commercialization’12 of operations, and may not result into a mission drift. However, the
chapter is a case study; and from its title; we wanted to learn from Grameen’s experience.
Among the numerous issues we wanted to improve from that chapter was the fact that we
analyzed a single Microfinance entity. The study also did not consider commercialization
aspects per se but focused on subsidy dependence and sustainability using the SDI, SDR
and SPM over 20 years. The chapter was essential as a building block to the current and
future chapters, as it responded to numerous research questions that existed even prior to
the completion of the comprehensive literature survey in the area. This chapter however
portrays vivid improvements from the previous one.
After thoroughly combing the ever growing microfinance literature, this study was
deemed necessary as it will be focusing in an area that is seen to be a microfinance
region13. The findings of this chapter seek to fill in the gap knowledge to the existing
literature as it is the first study (to the extent of our knowledge) of this kind to cover the
region; it is also the first paper attempting to take aboard commercialization variables in a
panel methodology; an interesting contribution is the outstanding work of Ferro Luzzi
and Weber (2006); which motivates thinking outside the box, as opposed to the over
replication of methodologies in microfinance and other areas. This chapter will also
contribute to formulate a basis for future research as we will point out the potential PRIs.
12
As per the definition of Woller.
Chambo (2003) refered to Tanzania as a microfinance country. This proposition based on the
characteristics of rural urban population distribution and financial service access; which indeed closely
resembles other East African nations.
13
12
Our intention was to study the cognitive dissonance around microfinance outreach and
sustainability in the economy of member countries of the EAC. The member countries of
the EAC include Burundi, Kenya, Rwanda, Tanzania and Uganda, we were however
unable to obtain relevant MFI data for Burundi, and had no other options but to drop it
from the study; we did however observe the real and financial indicators of the entire
member countries and appreciated that Burundi is indeed having comparable
characteristics with others in the region.
3
Data and Methodology
3.1
Theoretical Framework
The literature suggests a tradeoff between outreach and sustainability. This is the so
called mission drift. Outreach in this framework refers to the capacity of reaching the
financially excluded wherever and whoever they are. This entails serving the ultra poor
and the locationally disadvantaged population, including accessing rural and remote areas
of the economies: this indeed means deliberate MFI efforts including moving out of the
way through endeavoring to reach the clientele which would have otherwise remain
unreached.
Sustainability has been presented in numerous forms such as financial, operations and
relations sustainability. Attaining sustainability means delivering microfinance services
to the clients in a profitable manner without depending on subsidies. It means adopting
good practices and operating efficiently, with positive returns on productive assets
(preferably above the general rate of inflation). A sustainable MFI is the one which
operates profitably, does not require subsidies to succeed and efficiently responds to the
needs of the client so as to establish a client-MFI sustainable relationship.
Average loan and number of clients can be used as proxies in measuring outreach. The
average loan has been accepted as an indicator of outreach; whereas, the higher the
average loan the lower the outreach and vice versa. The traditional method is to compare
13
average loan to GDP and concluding that an average loan greater than the GDP is an
indicator that an MFI does not reach the poorest of the poor.
Figure 3:
Main proxies of microfinance outreach used in the study
(1) Average Loan
Microfinance
Outreach
(2) Number of Clients
(3) Dollar years of
borrowed resources
Microfinance outreach has depth and breadth. Average loan has been used as a proxy measure for depth
whereas number of clients has been a proxy for breadth. Dollar Years have been argued to be a better
representative of average loan as it encompasses all aspects of a loan size (Schreiner, 2001)
Source: designed from the arguments in the theoretical framework.
Obviously, the income distribution is uneven, as such; the poorest of the poor scramble
for 20% of the national income, this in turn makes what seemed to be low average loans
in the earlier scenario to gain a higher proportion; and it has been argued that it would be
more appropriate to measure average loan as a proportion of 20% of the GDP. A more
careful consideration prompts us to innovate a new bench mark, the poverty level
definition of US $2 a day to compute a synthetic GDP, which comes to approximately
$720; and technically speaking, an individual whose annual consumption is $720 is
classified as poor due to the fact the average consumption of $2 a day exceeds $720 in a
year. Generally Speaking, average loan has an inverse relationship with outreach; the
higher the average loan, the lower the outreach and vice versa. The average loan indicates
the depth magnitude of outreach.
The number of clients served by the MFI is also another indicator of outreach, where
there is a straightforward positive relationship between the two. The higher the number of
clients served by a MFI, the greater the outreach, the lower the number of clients the
lower the outreach. The number of clients is an indicator of the breadth magnitude of
14
outreach. We have identified that outreach is somewhat broad and can be measured by
depth and breadth.
The depth of outreach is a concept of reaching the grassroots; in this context ‘grassroots’
referrers to those individuals and households having low income. This is why we take
‘average loan’ as a measure of depth of outreach. As low income is relative, we identify a
benchmark of income, which is the GDP per capita (more discussion on this in section
3.3.1). Some of the non poor households and individuals may have been excluded from
the mainstream financial services for a number of reasons (such as geographical location)
whilst they are in dire need of these services. Services offered by MFIs target the
individuals and households who fall under this category, as such the general number of
customers served by MFIs are an indicator of breadth of outreach.
Figure 3 above summarizes the arguments presented in the preceding discussion, in a
nutshell; microfinance outreach is explained by average loan and the number of clients
served by the MFI. Other relevant variables that can be employed to explain outreach
such as demographic data describing features of the clientele can be modeled where
available. We regret that our study is facing limitations of not incorporating demographic
variables, this has been greatly contributed by the constraints in obtaining relevant data
and information; we leave that for future research.
The proxies for sustainability are much more complex to develop. We however peg
sustainability and efficiency and would expect a sustainable MFI to have lower per unit
costs and higher returns on both equity and capital employed.
Information of self sufficiency that takes into account both implicit and explicit subsidies
is unavailable, we therefore have no other option than to use ROA, ROCE and other
efficiency measures such as the cost per borrower, borrowers per personnel and
efficiency. Both ROA and ROCE are measures of profitability, sustainable MFIs should
have positive ROA and ROCE as an indication that they operate profitably. Strictly
speaking, negative ROA and ROCE does not translate into lack sustainability is a
straightforward manner as it may be otherwise interpreted from the afore explanation,
15
due to the fact that even profitable commercial undertakings may experience period/s of
negative returns. Our argument here is the fact that these two items are indicators of
sustainability all else remaining constant.
The cost per borrower is another indicator of sustainability, as it a sign of efficiency; it is
a unit cost of advancing financial service/s to a borrower. The lower the cost per
borrower, the higher the operational efficiency of the MFI; the more likely for it to being
sustainable in the long run. Likewise, the borrower per staff ratio represents the number
of borrowers served by one staff. The greater the number of borrowers served by a
member of staff, the greater the efficiency of the MFI and vice versa. The greater the
efficiency of the MFI, the higher the probability of being sustainable in the long run. The
proportion of operating expense over gross loan portfolio is yet another measure of
efficiency: The lower its proportion, the higher the operational efficiency of the MFI; the
more likely for it to being sustainable in the long run; the vice versa is also true.
Figure 4 endeavors to present the concept of sustainability; it outrightly suggests that it is
a complex paradigm, which itself is explained by numerous factors, which is summarized
as efficiency, commercialization and profitability. Where commercialization is said to
induce sustainability, there is a vicious circle of inducement between commercialization
and profitability on one hand and commercialization and efficiency on the other. The
figure also suggests an inducing relation between competition and commercialization on
one side and regulation and commercialization on another.
Commercialization is proxied by competition and regulation. A high level of competition
signifies greater commercialization and a low level of competition signifies a low level of
commercialization. Competition is characterized by suppliers chasing consumers, and in
our case, the MFIs running after potential clients, as such the MFIs start competing
between them. Regulation ensures that microfinance follows best practices in operation.
As such, it is expected that regulated MFIs have fulfilled the criteria of sustainability and
are permitted to offer a vast number of microfinance products, including accepting
16
deposits which is not permitted to unregulated MFIs. Our sample is characterized by both
regulated and unregulated MFIs as depicted in table 5.
Figure 4:
Relationships between Sustainability, its proxies and what induces it.
Sustainability
Induced by
Operational
Efficiency
Profitability
Commercialization
Competition
Regulation
Sustainability is proxied by operational efficiency and profitability. The former can be low cost per borrower, a high
borrower to staff ratio and low proportion of operational cost to gross loan portfolio. The later may take the form of
ROA, ROCE, Profitability Index and other measures of profitability. Commercialization is comprised of competition
and regulation, where competition is measured by concentration of biggest players in the market; regulation involves
adopting good practices overtime, as such it is influenced by age, governance and to high extent profitability.
Source: Designed from the arguments in the framework
As such, it is expected that regulated MFIs have fulfilled the criteria of sustainability and
are permitted to offer a vast number of microfinance products, including accepting
17
deposits which is not permitted to unregulated MFIs. Our sample is characterized by both
regulated and unregulated MFIs as depicted in table 5.
The usage of $years as an indicator of outreach was innovated by Schreiner (2001) into
the literature of microfinance, who argues that dollar years is a better measure of loan
size as it accounts for all major aspects of loan size14. Accordingly, the work of OlivaresPolanco (2005) is the only study that endeavored to model the cognitive dissonance
surrounding outreach and sustainability using ‘dollar years of borrowed resources’. The
empirical work employed a full model and reduced models, which is given in sub-section
3.3 titled ‘Estimation and testing procedures’.
3.1.1
Average loan as a function of other variables
We have identified various relationships of average loan with other variables in the
literature, whereas some studies were consistent with some of the relationships; others
had different findings, these drifts in the existing literature is the invocation of this piece
of work. MFI age has been the component into which researchers had differing opinions
as to its effect on the average loan portfolio. This paper endeavors to identify whether or
not age has an impact on average loan and the sign of the effect. Likewise we observe the
ROA coefficient as an explanatory variable; this will explain the relationship between
profitability as a proxy of sustainability and outreach: where average loan is treated as a
proxy of outreach. We also seek to present the way average loan behavior is generally
affected by the operating expense, this being an effort to scrutinize the impact of
efficiency.
Adherences to regulation and presence competition have been viewed to impact the
average outstanding loan in a positive way: this also explains that these two components
have an effect on average loan. We would also like to check the relationship between the
14
The aspects of loan size as presented by Schreiner (2001) are term to maturity, dollars disbursed, average
balance, dollars per installment, time between installments, number between installments and the dollar
years of borrowed resources. Schreiner suggests that dollar years of borrowed resources encompass all the
other aspects. $years has been argued in the contributions of by Schreiner (2001) as a better presentation of
the average loan in measuring depth of outreach. It is incorporated in this study as a measure of depth of
outreach so as to robust our results.
18
average loan on one hand and borrowers, personnel, GDP and the borrower to personnel
ratio. In strict sense, we have a functional relationship explained by (1) below.
AVLOAN  f ( ROA, EFF , REG, BORR, PERS, GDP, COMP, AGE, ROCE, COST / BORR)
Where:AVLOAN
=
Average loan outstanding
ROA
=
Return on Assets
EFF
=
Efficiency
REG
=
Regulation or no regulation
BORR
=
Average outstanding borrowers
PERS
=
Personnel (MFI staff)
COMP
=
Competition
AGE
=
Age
ROCE
=
Return on Capital Employed
GDP
=
The Gross Domestic Product per capita
COST/BORR =
3.1.2
Cost per borrower.
Average Outstanding Borrowers as a Function of other Variables
The number of borrowers reached is an indicator of outreach, the higher the number of
borrowers the greater the outreach and vice versa. With same objectives as in (1) above,
we seek to establish the functional relationship below:BORR  g ( ROA , EFF , REG , AVLOAN , AGE , PERS , GDP, COMP, ROCE )
(2)
19
(1)
Where:AVLOAN
=
Average loan outstanding
ROA
=
Return on Assets
EFF
=
Efficiency
REG
=
Regulation or no regulation
BORR
=
Average outstanding borrowers
PERS
=
Personnel (MFI staff)
COMP
=
Competition
AGE
=
Age
ROCE
=
Return on Capital Employed
GDP
=
The Gross Domestic Product per capita
COST/BORR =
3.1.3
Cost per borrower.
The implied relationships of other variables
We have developed two functional relationships in sections 4.1.1 and 4.1.2 which
explicitly imply that the outreach proxies are explained by other elements including
proxies for sustainability and commercialization. This relationship making outreach the
explained component is supported by the arguments of Rhyne (1998) who asserts that
‘there is only one (in microfinance operations) objective – outreach, sustainability is but a
means to achieve it’. Some of the identified and used independent variables in this study
have been argued to have positive impacts while others have been argued to have
negative impacts and others had no relationships or insignificant impacts as highlighted
by table 8.
Insert table 8 about here
20
3.2
Measurement and Data
The measurements of the variables in equations (1) and (2) is summarized and reported in
Table 3, where we endeavor to present the descriptions of the relevant variables and the
source of their information.
Insert table 3 about here
Data used in the study is from various sources. The market mix website
(http://www.mixmarket.org) has been the main source of information, where some
information was not available; and it was necessary to contact the relevant information;
the contact details were also provided. The World Bank, World Development Indicators
(WDI) September 2006 , ESDS International, (MIMAS) University of Manchester was
also another source of data; likewise individual MFIs were contacted for the purposes of
obtaining all the relevant data.
Our sample is based on the MFIs that are members of the market mix website with three,
four and five diamond rates. Despite the fact that market mix website endeavors to
provide a diverse set of information, there had been missing information; which we
managed to fill after establishing contact with the MFIs in question: this process had been
laborious, but we successfully managed to gather the bits and pieces and merged them
together. This process proved to be particularly difficult for newly established members,
likewise the members who were rated at one and two diamonds (less than three) had the
highest degree of missing gaps in the information provided. We swallow the bitter pill by
excluding members of less than three diamonds and non members of the market mix
website in this study due to data unavailability. Initially we had 41 (see Table4)
Microfinance Institutions whose data is for 6 years period (2000 – 2005), resulting to
observations that would exceed 240. However, we were forced to trim the number of
MFIs from 41 to 33 due to reasons that have already been pointed out.
The original sample had 15 regulated MFIs and 26 unregulated MFIs, likewise 24 of the
MFIs were offering savings products while 13 were merely providing credit, majority of
the MFIs that were providing saving products were at the same time regulated; in
21
Tanzania only regulated MFIs offer saving products, the situation is different in Uganda,
where a significant number of unregulated MFIs offer saving products (see table 5 for a
systematic breakdown). Eventually we developed a balanced panel structure of thirty (33)
MFIs for a period of six (6) years ranging from 2000 to 2006 summing to a total of 198
observations for this study using the available data under the constraints explained
earlier. The diamond classification by the market mix website is highly informative as the
fewer and fewer the diamond rate the less and less information available for the MFI. The
disclosure requirement for institutional reporting at the Mix Market enhances the data
quality.
Insert table 4 and 5 about here.
3.2.1
About the Mix MarketTM
The Mix Market is a global virtual microfinance platform providing information about
microfinance in diverse ways to various stakeholders. It was established with a view of
developing transparency of information and to link global MFIs with investors and
donors whilst at the same time promoting investments and information flows in this
developing sector. Until the early months of 2007, the Mix Market provides data on 864
MFIs, 147 partners and 88 investors.
Publicly launched in 2002 it is a key component of the MIX (Microfinance Information
eXchange): its previous name was the Virtual Microfinance Market (VMM) established
by the UNCTAD and was pioneered by the same with CGAP; it is currently one of the
most informative website on microfinance issues.
Information provided on MFIs includes outreach and impact data (where available),
audited financial statements and other general and contact information. There is some
information about the donor/investor portfolio and the general country information. This
virtual resource hub was created with duality in mind: firstly, it was seeking to facilitate
the comparison of financial and outreach performance of MFIs. Secondly, it aimed to
magnetize more public and commercially oriented investors to microfinance through the
22
promotion of financial transparency, accountability and increased disclosure standards.
There has been a spontaneous increment of parties benefiting from information provided
by Mix Market and the number of people visiting the online resource has also increased
over the past few years.
3.3
Estimation and Testing Procedures
Our approach is inspired by the contemporary work of Olivares-Polanco (2005) that
focused on presenting commercialization factors in Latin America. We build on it and
take it a step further as his study did not have a panel structure but rather a single year
cross section; likewise it was not for the same year across all the cross sections but two
different years.
One of the key simplifying assumptions will be the treatment of loan size to determine
the depth of outreach, the larger the loan size the lower the outreach and vice versa. The
usage of loan size as an indicator of outreach has been widely used in literature such as
(Olivares Polanco, 2005; Schreiner, 2002, Ferro Luzzi and Weber, 2006; Christen 2001),
in some cases, it has however been used with a disclaimer15. We shall conduct an OLS to
test the conclusions of Olivares-Polanco (2005) in the East African vicinity. We shall
thus be in a position to contribute to the existing knowledge, by explaining the scenario in
East Africa. Our tests will be on whether institution type, institution age and competition
may be significant enough in explaining loan size, as well as other relevant dependent
variables as explained in (1) and (2) of the theoretical framework.
This will help us to either validate or reject the controversy that average loan increases
with age16. Likewise we observe the ROA coefficient as an explanatory variable, this will
explain the relationship between profitability as a proxy of sustainability and outreach.
We also seek to identify the way average loan behavior is generally affected by the
15
state
Christen (2001) suggests that increase in average loan is not a sign of mission drift and that age of the
MFI including the age of MFI-Client relationships may imply that future loans following successful
repayment increase compared to past and present loans. The empirical analysis of Olivares-Polanco (2005)
found that loan size decreases with age. This is a very crucial strand in the literature of microfinance in
Latin America.
16
23
operating expense, this being an effort to scrutinize the impact of efficiency. Likewise the
number of personnel hired by the MFI is likely to be induced by previous activities,
which includes the number of borrowers served in the previous periods.
We develop several equations that will enable us to test the theoretical framework
developed in section 3. We shall run econometric regressions so as to provide some
explanation of the cognitive dissonance surrounding outreach and sustainability of MFIs
in East Africa, by developing econometric relationships using the functional forms
suggested by equations (1) and (2). We proceed on to model average loan as a proxy of
outreach depth and subsequently average number of borrowers as a proxy for outreach
breadth using numerous model variants as follows:3.3.1
Modeling Average loan as a proxy for outreach
The theoretical specification in equation (1)
with respects of defining average
outstanding loan as an indicator for outreach can now be expressed in an empirical form
in terms of four empirical model variants as follows:3.3.1.1
Average loan as a dependent variable
The relationship expressed here seeks to probe the theoretical framework in an empirical
manner. It will try to identify which and how the independent variables affect the
dependent variable. It is a measure of outreach. Our regression equation with this regard
is expressed as follows:ln AVLOAN it   0  1 ROAit   2 ln EFFit   3 REGit   4 ln PERS it
  5 ln Comp it   6 ln AGEit   7 ln COST / BORRit   it
(3)
Where:AVLOAN
=
Average loan outstanding
ROA
=
Return on Assets
EFF
=
Efficiency
24
REG
=
Regulation or no regulation
BORR
=
Average outstanding borrowers
PERS
=
Personnel (MFI staff)
COMP
=
Competition
AGE
=
Age

=
is the error term
ROCE
=
Return on Capital Employed
ln
=
The natural logarithm
Subscripts I
=
denotes the unique MFI, starting from the first (1st)
MFI to the thirtieth (30th) MFI.
Subscripts t
=
denotes the time period for each period under the
study, from 2000 to 2005.
The preconditioned signs of the coefficients for each variable are explained in the
theoretical framework and supported by the literature17, although; in some cases there are
findings of drifts.
3.3.1.2
Average loan proportion to GDP as a dependent variable
For purposes of making international comparisons of outreach between MFIs the average
loan as a proportion to the GDP has been recommended to use as an independent variable
(Schreiner, 2001). In our study, we focus on East Africa as a region and do not intend to
report our findings as a country by country findings, but a combination of all the
countries whose MFIs were used in the sample; a similar study using the same dependent
variable is that of Olivares-Polanco (2005), who analyzed commercialization factors in
Latin America. The regression equation for estimation of the outreach relationship is
given hereunder:17
See table
25
ln( AVLOAN / GDP) it   0   1 ROAit   2 ln EFFit   3 REGit   4 ln PERS it
  5 ln Compit   6 ln AGEit   7 ln COST / BORRit   it
(4)
Where:
ln(AVLOAN/GDP)
= Natural logarithm of average outstanding loan to GDP
Equation (4) is similar to equation (3) in all respects with the exception that the
numerator of dependent variable is having GDP as its denominator. We use this equation
as a matter of accommodating theoretical arguments which contend that average loan has
to be taken in relation to the GDP per capita of the country in question to measure depth
of outreach in an effective manner.
3.3.1.3
Average loan as a proportion to 20% of the GDP as a dependent variable
Another argument in the literature is that there is unequal income distribution such that
the lowest earners in the economy share only about 20% of the GDP. This argument
asserts that the reported GDPs of the economies cannot be used as a benchmark for
estimating average annual earnings as the inequality in distribution of income is ever
present and the best estimate for average annual earnings for a poor person would be 20%
of the GDP.
We model this theoretical equation as follows:ln( AVLOAN / 20%GDP) it   0  1 ROAit   2 ln EFFit   3 REGit   4 ln PERS it
  5 ln Comp it   6 ln AGEit   7 ln COST / BORRit   it
(5)
The arguments of using this dependent variable are indeed a good indicator of deep
outreach, and have been presented as such. We run this regression to test the robustness
of the analysis done so far, we also seek to test the theory and report what the data tells
us. Putting the econometric arguments aside, what we really want to observe are the
potential changes in the coefficients under consideration as they surely assist us in the
interpretations of our findings.
26
3.3.1.4
Average loan as a proportion of the synthetic poverty level GDP
Using average loan proportions to either GDP or 20% of GDP tends to distort the reality
of outreach by the MFIs in poor economies; as most of the GDPs reported, when
compared to the global poverty indicator of consuming less than $2 a day are themselves
below the global poverty line. In response to this staggering reality, this study has
developed a ‘synthetic poverty level GDP’ of $720 which has been argued and presented
in the theoretical framework as another innovation that comes with analysis conducted in
this piece of work.
The equation representing the theoretical specification explained above is given below:ln( AVLOAN / SYNTH GDP) it   0   1 ROAit   2 ln EFFit   3 REGit   4 ln PERS it
(6)
  5 ln Compit   6 ln AGEit   7 ln COST / BORRit   it
3.3.2
Modeling Average outstanding borrowers as a proxy of outreach
The theoretical specification in equation (2) with respects of defining average outstanding
borrowers as an indicator for outreach can now be expressed in an empirical form in
terms of an empirical model as follows:ln BORRit   0   1 ROAit   2 ln EFFit   3 REGit   4 AVLOAN it   5 AGEit
  6 PERS it   7 COMPit   8 COST / BORRit   9 BORR / PERS it 1   it
(7)
The above equation is takes into account all theoretical relationships, and can also be
expressed while dropping the lagged value of borrower to personnel as follows:ln BORRit   0   1 ROAit   2 ln EFFit   3 REGit   4 AVLOAN it   5 AGEit
  6 PERS it   7 COMPit   8 COST / BORRit   it
3.3.3
(8)
Average loan percentage proportion to 20% GDP indicator
As an effort of complementing section 4.3.1.3, we shall present general information about
average loan; here we start by explaining the theoretical implications of average loan
27
proportion to 20% of GDP. We shall make full explanation in the analysis and
interpretation section.
A quick observation of the summary statistics (see table 6) provides interesting
information. The average loan as a proportion to GDP had a mean of 0.88, a minimum
value of 0.09 and a maximum value of 4.35; which suggests that on average the borrower
takes a loan which equates 88% of the GDP, and the largest average loan was around
435% of the GDP and the lowest being an average of 9% of the GDP. The data indicate
different degrees of outreach with high standard deviations; if for the moment we
continue to assume that the lower the average loan the higher the outreach and vice versa,
then the MFI offering the average loan of 9% to GDP is clearly having deeper outreach.
As the average is 0.88, the quick interpretation is that it has a deep outreach. The above
interpretation will change when we take into account the fact that the national wealth is
unfairly shared by the population, with the lowest earners who exceed 40% of the total
population scrambling for 20% of the national income. This becomes vivid when we
observe the statistics of average loan as a proportion to 20% of the GDP.
3.4
Using Dollar years of borrowed resources to enrich and robust our study.
We hinted on the possibility of using dollar years of borrowed resources in modeling. An
observation of the measurement dollar years of borrowed resources indicates that there is
a relationship between the computed dollar years of borrowed resources with the average
loan and number of borrowers. The dollar years of borrowed resources is computed using
the formula below:-
$ years 
Average Annual Dollars held by clients
Number of loans disbursed in a year
(9)
However, as the $years by definition is a different variable to the average loan and
borrowers, we use this approach to present our findings using three model variants, pretty
similar to the three models we developed in equations (3), (4) and (5), taking the equation
28
(9), (10) and (11) respectively: the only difference is the dependent variable being either
the as follows:
3.4.1
$Years as a dependent variable
The model specification for dollar years of borrowed resources as a dependent variable is
given below:ln $YEARS it   0   1 ROAit   2 ln EFFit   3 REGit   4 ln PERS it
  5 ln Comp it   6 ln AGEit   7 ln COST / BORRit   it
3.4.2
$Years proportion to GDP as dependent variable
ln($YEARS / GDP) it   0   1 ROAit   2 ln EFFit   3 REGit   4 ln PERS it
  5 ln Comp it   6 ln AGE   7 ln COST / BORRit   it
3.4.3
(11)
$years proportion to 20% of GDP as dependent variable
ln($YEARS / 20%GDP) it   0   1 ROAit   2 ln EFFit   3 REGit   4 ln PERS it
  5 ln Comp it   6 ln AGEit   7 ln COST / BORRit   it
3.5
(10)
(12)
Robustness of the analysis
We estimate the regressions by exhausting all the proxies of outreach from the available
theory. We compare them so as to determine whether all proxies can yield similar results
to robust our findings. We also employ full model specification as indicated in the above
models and reduced model specifications. We report our findings for the East African
community as an aggregated block.
3.6
Econometric considerations
We structure the information relating to the MFIs in our sample in a panel format. The
benefits of utilizing panel analysis are undoubtedly high as panel data is famous for
29
controlling individual heterogeneity, gives more informative data, more variability, more
degrees of freedom and more efficiency. Panels also enables construction and tests of
more complicated behavioral models compared to other data, make possible the study of
dynamics of adjustment and allows identification and measures of effects which would
have otherwise not been detectable in the analysis of cross section or time series data18.
We report results for panel least squares and cross section random effects after testing for
fixed effects which was rejected, and considering the suggestions of Chamberlain (1984)
and Hausman and Taylor (1981). The summary statistics of our data is presented in Table
6, while Table 7 presents the correlation matrix of the variables.
Insert tables 6 and 7 about here
3.6.1
Limitations of Our Study
As is the case of various research studies, ours is of no exception and we summarize
some of the limitations of our study as follows:3.6.1.1
Sample:
As pointed out, our sample uses MFIs registered with the market mix
website. Clearly, the selection is not random. With limited number of
MFIs and time series available on them; we had little choice left as to the
selection of a representative sample. A cross regional survey would be
needed to collect relevant primary data.
3.6.1.2
Methodology:
We fail to carry out extensive analysis as it would have been the case if
we had a large sample. Some of the statistical operations demand rich data
structures which can sacrifice a considerable number of degrees of
freedom.
18
For a lengthily discussion on this aspect see Baltagi (2005) pp 4-7
30
3.6.1.3
As indicated above, a cross-regional survey would be necessary to collect
the relevant data, unfortunately; with financial and time constraints, we
cannot be in a position to obtain findings which suffer from no limitations.
With development of regulatory practices and encouragement of MFIs to exercise
transparency, we are optimistic that future research will overcome limitations faced by
our study.
4
Discussion of Results
We interpret the results of this study in the following way. The data used in the analysis
suggests that microfinance age does not have a significant influence on neither depth nor
breadth of outreach. It however appears that microfinance age has an inverse relationship
with average loan divided by GDP per capita; this in turn suggests that microfinance age
can improve depth of outreach when considering uneven income distribution; this is
consistent with the findings of Olivares Polanco (2005). The findings are also consistent
with our earlier findings from Grameen, where we saw the bank’s age and depth of
outreach are positively related.
The sign of the coefficient for the level of competition suggests that the higher the
concentration (or the lower the competition) the lower the loan size. Should it be that the
variable predicts loan size accurately, then there is a positive relationship between
competition and loan size. This effectively means that more competition will result to
MFIs offering higher loan size, which probably implies that the competing MFIs will be
searching for more profitable clients, who are in most cases, the elite of the poor. This
finding is also consistent with findings of vast studies that microfinance serves the welloff of the poor and does not successfully target the ultra poor and the destitute.
Concentration coefficient suggests a positive relationship between competition and
breadth of outreach, obviously indicating that more clients are served by MFIs in
presence of competition.
31
The depth of outreach seems to be negatively affected by cost per borrower, as the
coefficient sign of cost per borrower is positive suggesting that a higher cost per borrower
will result to a higher loan all else being equal. This observation suggests that providing
large microfinance loans may require high monitoring, evaluation and follow-up costs.
MFIs will hedge themselves against potential default losses, especially from non
compliance of repayment in respect of big loans.
The coefficient of operating expense to loan portfolio has a negative relationship with
loan size. This implies that a higher operating expense ratio to loan portfolio will result to
lower average loan and vice versa, meaning that in order for MFIs to attain a deep
outreach, the costs of operations have to be higher than if it was not aiming for greater
outreach. This again, is consistent with theoretical arguments which contend that it is
cheaper for banks to provide a large loan than several tiny loans. In most cases, the
administrative (clerical paper work) workload for a big loan and tiny loan is almost equal.
Likewise individuals/households applying for tiny loans fail to meet conventional
banking requirements and may therefore require further scrutiny and sometimes even
financial training before being provided with credit; this simply adds up to operating
costs.
We also observe the coefficient of operating expense to loan portfolio having a negative
relationship with number of borrowers (clients). This simply indicates that in order for a
MFI to expand its breadth, it necessarily needs to be efficient in terms of minimizing its
and vice versa, this is as per the coefficient of the personnel; however this observation is
not significant in all empirical regression models carried out in this study. We also
observe a positive relationship between the number of employees and the number of
borrowers (clients); as microfinance grows, it tends to employ more personnel, who serve
a greater number of clients.
The average loan appears to have a positive relationship with regulation. The findings
appear to be consistent with the theory which suggests regulation will lead to higher
average loans and decrease the depth of outreach although the coefficients are not
significant.
Regulation is observed to negatively affect the number of borrowers
32
(clients), which shows that regulations have an inverse relationship with breadth of
outreach. The coefficients of regulation from the data in our sample are however not
significant.
The coefficient for the ROA variable appears to have a negative relationship with average
loan. This simply means only when a MFI is having a high ROA than it can afford to
offer lower loans. This is probably done when the MFI feels that it can subsidize its
additional costs of deepening outreach from existing and potential profitability so as to
attain the dual faced mission. The breadth of outreach appears to be negatively related
with ROA, which implies that an expanded operation to encompass wider client base
might necessarily compromise the ROA, this again may be as a result of taking aboard
more clients at the expense of observing their creditworthiness. The ROA coefficients
appear to be significant only in the case where uneven income distribution is taken into
consideration, as regression is done when the average loan proportion to 20% of GDP
was the explained variable. In this scenario, ROA was seen to have a positive impact on
depth of outreach.
The breadth of outreach is observed to have a negative relationship with average loan,
implying that the higher the average loan the lower the breadth of outreach and vice
versa. This may be explained that greater loans are offered by conventional banking
institutions at better terms than MFIs, as such clients requiring higher loans would rather
borrow from a commercial bank. The MFI would only give higher loans to those clients it
believes are creditworthy to the highest standards; but once a borrowers possesses those
standards, she can easily get an advance from the banks.
5
Conclusions and Recommendations
We have examined the cognitive dissonance around microfinance outreach and
sustainability using panel data for 33 MFIs of four East African countries. The sample
was observed for six years and we had 198 observations. Particularly we were interested
to study what determinants of depth and breadth of outreach. We used average loan, and
other average loan expressions namely average loan as a proportion to GDP, average loan
33
as a proportion to 20% of GDP, average loan as a proportion to synthetic GDP and the
sum of dollar years of borrowed resources as a representative of average loan as a proxy
of depth of outreach. The number of borrowers was utilized as a measure of breadth of
outreach.
Among the objectives of our study was to determine the impact of age on loan size,
where we observed an inverse relationship between age and average loan, this
observation invalidates the arguments of Christen (2001) and concur with the findings of
Olivares Polanco (2005). We explain that older MFIs tend to be better experienced, and
assuming that they adhered to best practices; they are likely to have developed their
muscles in terms of financial sustainability; as such, they can be in a better position of
providing lower average loans to their borrowers in bid to attain the duality mission.
Microfinance competition is observed to be affect depth of outreach in a negative way.
Again, this observation is consistent with the findings of Olivares Polanco (2005) and
confirms the old belief of mission drift. Provision of microfinance services is a form of
market intervention in its own right and tends to disturb the equilibrium Aghion and
Morduch (2005), as such encouraging competition in the intervened market is similar to
the desire of eating the cake and yet keeping it at the same time. Much as we should
encourage competition in MFI operations, this should not be the main objective and
should be a gradual process as it may end up cannibalizing the primary objective that
resulted into the microfinance movement.
The operating expense to loan portfolio relationship with average loan has been observed
to be negative and possible explanations of that have been pointed out in the previous
section. In order for MFIs to have a deeper outreach, they must incur higher operating
costs, which involve payment of salaries, research and development, clerical work of loan
processing etc. Our findings in the previous chapter suggest using subsidies in a manner
that would be effective and efficient, as such; we recommend partial subsidization of
MFIs operating costs to deepen outreach.
34
The data utilized in this study suggest that regulation and ROA appear not to be
significant in explaining depth of outreach in most of the regressions. However, where a
significant influence has been observed is that regulation is positively related to average
loan, confirming the old idea of mission drift. Where ROA was significant, it appeared to
have a positive relationship with depth of outreach as the higher the ROA the lower the
average loan and vice versa. Only operational and financial efficient MFIs are likely to
produce a positive and considerable large ROA, and in many cases, MFIs that adhere to
best practices are both operational and financial efficient. We view efficiency and
subsidy usage to be independent events in a way such that microfinance institutions can
be efficient while at the same time using subsidies. In this case, we recommend MFIs to
aim for attainment of efficient and be seen as such, and MFIs that are oriented towards
efficiency may be allocated subsidies, in way that will ensure that they do not loosen their
standards.
Investigation on the relationship between average loan and number of clients need to be
investigated in greater detail, perhaps there will be uncovered an interesting relationship
that will make a breakthrough in the research of this area of microfinance; there is a
probability of observing interaction between the two proxies of outreach. This study can
be improved by encompassing more MFIs in the sample that do not report to the mix
market, albeit the cost and time constraints of gathering relevant data may be high.
The efficiency variable used in the study can either replaced by a new variable OSS as
used by Hartarska (2004) or used simultaneously along with other information. A
replicated study using data for a different region will be valuable to compare the findings
and contributing to the existing literature.
35
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39
Table 1:
Real and Financial Indicators for 2000, 2005 and changes
Tanzania
Kenya
Section A
GDP (constant 2000 US$)
Rwanda
Uganda
2000
9.1 bil
12.7 bil
1.8 bil
5.9 bil
5.1
0.6
6
5.6
261.2
414
225.7
243.8
Deposit interest rate (%)
7.4
8.1
8.9
9.8
Interest rate spread
14.2
14.2
N/A
13.1
Inflation, conser prices (annual %)
5.9
10
4.3
2.8
Inflation, GDP deflator (annual %)
7.5
6.1
3.3
3.8
GDP growth (annual %)
GDP per capita (consnt 2000 US$)
Section B
GDP (constant 2000 US$)
2005
12.6 bil
14.7 bil
2.3 bil
7.7 bil
7
2.8
5
5.6
329.9
428.4
257.8
267.4
Deposit interest rate (%)
4.7
5.1
7.9
8.8
Interest rate spread
10.4
7.8
Inflation, consumer prices (annual %)
8.6
10.3
9.1
8.2
Inflation, GDP deflator (annual %)
3.7
3.7
7
8.6
GDP growth (annual %)
GDP per capita (consnt 2000 US$)
Section C
11
%(2005-2000)
GDP (constant 2000 US$)
39%
16%
29%
30%
GDP growth (annual %)
37%
367%
-16%
-1%
GDP per capita (consnt 2000 US$)
26%
3%
14%
10%
Deposit interest rate (%)
-36%
-37%
-11%
-11%
Interest rate spread
-27%
-45%
N/A
-16%
Inflation, conser prices (annual %)
46%
3%
113%
189%
Inflation, GDP deflator (annual %)
-50%
-39%
114%
126%
Note
Data in sections A and B has been extracted from the WDI September 2006 Database
from MIMAS, data in section C is calculated by author from sections A and B.
40
Table 2:East African Population and Poverty Indicators for years 2000 and 2005
Tanzania Uganda
Section A
Kenya
Rwanda
2000
Population, female (% of total)
50.4
50.1
50.2
51.7
Population growth (annual %)
2.1
3.1
2.2
6.8
34.7M
24.3M
30.7M
8.1M
Poverty headcount ratio at $1 a day (PPP) (% of pop)
57.8
N/A
N/A
51.7
Poverty headcount ratio at $2 a day (PPP) (% of pop)
89.9
N/A
N/A
83.8
Rural population growth (annual %)
1.6
3
2.1
5.5
Rural population (% of total population)
77.7
87.9
80.3
86.2
Urban population
7.8M
2.9M
6.1M
1.1M
Urban population growth (annual %)
3.7
3.8
2.9
15.1
Urban population (% of total)
22.3
12.1
19.7
13.8
Population, total
Section B
2005
Population, female (% of total)
50.2
50
49.9
51.5
Population growth (annual %)
1.8
3.5
2.3
1.7
38.2M
28.8M
34.2M
9.1M
Poverty headcount ratio at $1 a day (PPP) (% of pop)
N/A
N/A
N/A
N/A
Poverty headcount ratio at $2 a day (PPP) (% of pop)
N/A
N/A
N/A
N/A
Rural population growth (annual %)
1.3
3.4
2.1
80.7
Rural population (% of total population)
75.8
87.4
79.3
0.4
Urban population
9.3M
3.6M
7.1M
1.7M
Urban population growth (annual %)
3.4
4.3
3.3
7.6
Urban population (% of total)
24.2
12.6
20.7
19.3
Population, total
Note
Data in sections A and B has been extracted from the WDI September 2006 Database
from MIMAS.
41
Table 3
S/N
1
2
3
4
5
Measurements and definitions of Variables and sources of data
Variable
Avloan
Description
Average loan
This is the average outstanding loan; it is
measured as the summation of total
outstanding loan portfolio at the beginning
of the year and the end of divided
corresponding number of outstanding
borrowers.
Avloan%GDPc
Average loan
Is the percentage proportion of the average
presented as a
outstanding loan to the Gross Domestic
proportion of the Product per capita. Loans higher than the
GDP
per capita income can be interpreted as high
and vice versa when viewing depth.
Avloan20%GDPc Average loan
Is the percentage proportion of the average
outstanding loan to twenty percent of the
presented as a
proportion of 20% Gross Domestic Product per capita. As
of the GDP
income distribution is uneven, GDP per
capita is not a true representation of income
distribution; as such this variable looks at
further depth.
GDP
Gross Domestic
The Gross Domestic Product per capita. The
Product per
GDP used is for constant prices using 2000
Capita
as the base year.
Pers
Personnel
Is the number of staff employed by the
microfinance institution. This number is
crucial for determining the operational
efficiency of the MFIs. The figures are taken
at year ends.
Source
Market mix database for most data;
where the information in the database
was
incomplete,
the
respective
organization was contacted to provide
missing information.
Market mix database, for the numerator
(And in most cases the denominator).
The World Bank’s WDI (Word
Development Indicators) Database for
the denominator.
Market mix database, for the numerator
(And in most cases the denominator).
The World Bank’s WDI (Word
Development Indicators) Database for
the denominator.
The information of GDP per capita was
obtained from the World bank’s World
Development Indicators Database.
Market mix database for most data;
where the information in the database
was
incomplete,
the
respective
organization was contacted to provide
missing information.
Continue overleaf/..
42
Continued from previous page
Borrowers
6 Borr
7
Borr/Staff
8
Cost/Borr
9
Reg
10 ROA
Borrowers per
staff
Is the total number of active borrowers Market mix database for most data;
served by the microfinance institution. The where the information in the database
figures are taken at year ends
was
incomplete,
the
respective
organization was contacted to provide
missing information.
Is the borrower to staff ration. This ratio
explains the number of borrowers served by
a staff in a MFI. It may be seen as an
indicator of efficiency. The numbers are
taken at year ends.
Market mix database for most data;
where the information in the database
was
incomplete,
the
respective
organization was contacted to provide
missing information.
Is the cost per borrower; it is computed as
the total relevant cost of supplying credit
divided by number of active borrowers in a
given period. The cost has been derived
from year end reports adjusted to 2000
constant prices.
Market mix database for most data;
where the information in the database
was
incomplete,
the
respective
organization was contacted to provide
missing information.
Regulation.
Is a dummy variable where 1 represents the Market mix database.
microfinance institution is regulated and 0
otherwise. It is one of the commercialization
factors.
Return on Assets
Is the return on assets; computed as the
operating profits divided by outstanding
assets. It represents the level of economic
profitability. All data had to be adjusted to
2000 constant prices.
Market mix database for most data;
where the information in the database
was
incomplete,
the
respective
organization was contacted to provide
missing information.
Continue overleaf/..
43
Continued from previous page
Efficiency
11 Eff
12 Comp
Competition
14 Age
Age
15 $ years
Dollar years of
borrowed
resources
Refers to efficiency, computed as operating
expense divided by the period’s gross loan
portfolio. All prices are taken at year ends
and adjusted to 2000 constant prices.
Refers to the level of competition measured
as the concentration of the nation’s three
largest microfinance institutions. Due to the
lack of access to the true population, we
used the contents of the market mix database
as a sample for the true population of
microfinance institutions.
This is the age of the MFI in years from year
of inception. It counts a whole year in the
year of inception as it assumes the
operations were initiated at the start of the
period.
It is computed as average annual dollars
held by clients divided by number of loans
disbursed during the period. It is used as a
proxy for average loan.
Market mix database for most data;
where the information in the database
was
incomplete,
the
respective
organization was contacted to provide
missing information.
Market mix database for most data;
where the information in the database
was
incomplete,
the
respective
organization was contacted to provide
missing information.
Market Mix Website
Market mix database for most data;
where the information in the database
was
incomplete,
the
respective
organization was contacted to provide
missing information
44
Table 4:
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
List of MFI’s consulted in the regional survey
Name of MFI
Ebony Foundation
Equity Bank, formerly Equity Building Society
Faulu – Kenya
Kenya Agency to Development of Enterprise and Technology
Kenya Post Office Savings Bank
K-Rep Bank
Kenya Women Finance Trust
Microenterprise Development Services Ltd / formerly Sunlink
Rural Agency for Development
SEED Development Group
Small and Microenterprise Project
Ufanisi Afrika
Women Economic Empowerment Consort
Window Development Fund
Akiba Commercial Bank Ltd
FINCA Tanzania
Mbinga Community Bank
PRIDE Tanzania
Presidential Trust Fund
Small Enterprise Development Agency
Small Enterprise Foundation (Tanzania)
Centenary Rural Development Bank Ltd
Commercial Microfinance Limited
Country
Kenya
Kenya
Kenya
Kenya
Kenya
Kenya
Kenya
Kenya
Kenya
Kenya
Kenya
Kenya
Kenya
Kenya
Tanzania
Tanzania
Tanzania
Tanzania
Tanzania
Tanzania
Tanzania
Uganda
Uganda
Year Incepted
2000
1984
1992
2000
1978
2000
1982
1999
2004
2002
1975
2003
1999
2001
1997
1998
2003
1994
1984
1996
2000
1983
2000
Regulation Savings














































Continue overleaf/..
45
Continued from previous page
Name of MFI
Country Year Incepted
24 Faulu Uganda
Uganda
1995
25 FINCA Uganda
Uganda
1992
26 Foundation for Credit and Community Assistance
Uganda
1996
27 Initiative of Small Scale Industrialists Rural Savings & Credit Ltd
Uganda
1996
28 Kyamuhunga Peoples' Savings & Credit Development Association Ltd
Uganda
2001
29 Kiwafu Savings & Credit Cooperative Society Ltd.
Uganda
1992
30 Kamukuzi Village Trust Ltd
Uganda
1999
31 Micro Enterprise Development Network
Uganda
1997
32 Muhame Financial Services Cooperative Ltd.
Uganda
2001
33 Masaka Microfinance Development Cooperative Trust Ltd
Uganda
1999
34 PRIDE Uganda
Uganda
1995
35 Rubaare Modern Rural Savings & Credit Development Association Ltd. Uganda
2000
36 Shuuku Cooperative Savings & Credit Society Ltd.
Uganda
1999
37 Uganda Microfinance Limited
Uganda
1997
38 Uganda Finance Trust Limited
Uganda
1984
39 Volunteers Action for Development
Uganda
1996
40 Centre Financier aux Entrepreneurs S.A. AGASEKE
Rwanda
2003
41 Urwego
Rwanda
1997
Aggregate MFIs that are regulated in this sample
Aggregate MFIs that are not regulated in this sample
Total of regulated and unregulated
Aggregate MFIs that offer saving products in this sample
Aggregate MFIs that do not offer saving products in this sample
Total of saving and non saving offering MFIs
Compiled by Author from Market Mix Website at http://www.mixmarket.org accessed on 15th March 2007
Regulation Savings




































15
26
41
27
14
41
46
Table 5:
Country by Country Breakdown of surveyed MFIs’ Characteristics
(1)
(2)
(3)
(4)
(5)
KENYA
RWANDA
TANZANIA
UGANDA
TOTAL
A
Regulated
5
1
2
7
15
B
Unregulated
9
1
5
11
26
C
Total (a+b)
14
2
7
18
41
D
Savings
8
1
2
16
27
E
No Savings
6
1
5
2
14
F
Total (d+e)
14
2
7
18
41
G
Regulated and Savings
5
1
2
7
15
H
Unregulated and Savings
3
0
0
9
12
I
Regulated no Savings
0
0
0
0
0
J
Unregulated no savings
6
1
5
2
13
K
Total (g+h+i+j)
14
2
7
18
41
Information Presentation Using Sets
Table designed by Author from information on Table 4
47
Table 6:
Descriptive Statistics of Variables in the Sample
Table 6 presents descriptive statistics of the sample. The sample was gathered across four countries from the Eastern Part of Africa namely;
Tanzania, Kenya, Uganda and Rwanda. The Table is presented in three sections (a), (b) and (c), which present summary statistics for Tanzania &
Kenya, Uganda & Rwanda and the regional aggregate respectively.
Section (a)
Tanzania
Kenya
Mean
Median
Max
Min
S.Dev.
Obs
Mean
Median
Max
Min
S.Dev.
Obs
AvLoan/20%GDP
per Capita
6.46
2.59
32.16
0.86
8.58
36
2.80
2.02
10.23
0.03
2.83
60
Age
8.67
7.00
22.00
1.00
5.53
36
12.62
8.00
31.00
1.00
9.84
60
Average Loan
Avloan/GDP per
Capita
325.41
123.00
1679.00
45.00
493.92
36
324.88
267.50
876.00
39.00
210.14
60
1.12
0.41
6.43
0.17
1.76
36
0.77
0.63
2.05
0.09
0.50
60
Borrowers
18648
11201
71315
1038
19630
36
17325
9013
110112
168
22673
60
Borrowers/personnel
157
178
272
48
67
36
127
130
256
1
59
60
Competition
0.48
0.48
0.54
0.44
0.03
36
0.46
0.46
0.52
0.40
0.04
60
Cost per borrower
96.97
63.45
301.00
36.10
88.02
36
855.90
92.36
7818.90
31.60
2312.90
60
Efficiency
0.49
0.44
1.32
0.16
0.25
36
0.49
0.36
1.51
0.15
0.34
60
Personnel
108
68
290
12
88
36
421
421
428
414
5
60
294.33
293.00
333.00
261.00
24.82
36
246.92
91.50
1542.00
2.00
408.45
60
Regulation “Dummy”
0.17
0.00
1.00
0.00
0.38
36
0.30
0.00
1.00
0.00
0.46
60
ROA
-0.01
0.00
0.08
-0.21
0.06
36
0.02
0.02
0.53
-0.48
0.17
60
GDP per Capita
48
Section (b)
Uganda
Rwanda
Mean
Median
Max
Min
S.Dev.
Obs
Mean
Median
Max
Min
S.Dev.
Obs
AvLoan/20%GDP
per Capita
3.95
2.80
16.16
0.45
3.31
96
0.86
0.87
0.97
0.76
0.09
6
Age
9.13
7.00
24.00
1.00
6.12
96
0.17
0.17
0.19
0.15
0.02
6
Average Loan
Avloan/GDP per
Capita
209.43
148.00
916.00
22.00
177.31
96
6.50
6.50
9.00
4.00
1.87
6
0.81
0.58
3.43
0.09
0.68
96
41.83
41.00
50.00
36.00
5.38
6
Borrowers
12066
8088
57880
38
13655
96
11610
11575
17547
5047
5099
6
Borrowers/personnel
119
105
386
6
76
96
169
178
216
120
36
6
Competition
0.41
0.41
0.43
0.38
0.02
96
0.62
0.62
0.68
0.55
0.05
6
Cost per borrower
98.31
74.05
545.10
18.80
84.92
96
34.33
34.00
41.00
28.00
5.35
6
Efficiency
0.53
0.46
1.32
0.17
0.24
96
0.80
0.78
0.96
0.70
0.10
6
Personnel
112
55
847
3
160
96
66
61
101
42
22
6
255.50
256.00
267.00
244.00
7.85
96
242.33
244.50
258.00
226.00
11.91
6
Regulation “Dummy”
0.38
0.00
1.00
0.00
0.49
96
0.00
0.00
0.00
0.00
0.00
6
ROA
-0.02
0.01
0.17
-0.62
0.14
96
-0.17
-0.18
-0.02
-0.31
0.09
6
GDP per Capita
49
Section (c)
East Africa
AvLoan/
20%GDPPc
Age
Avloan
Avloan/GDPPc
Borr
Borr/Pers
Comp
Cost/borr
eff
Personnel
GDPPc
Reg
ROA
Mean
3.96
10.02
260.42
0.84
14843
130
0.44
325.59
0.52
150
312.31
0.30
-0.01
Median
2.63
7.00
161.00
0.55
9292
125
0.42
72.80
0.44
73
267.00
0.00
0.01
Maximum
32.16
31.00
1679.00
6.43
110112
386
0.68
7818.90
1.51
1542
428.00
1.00
0.53
Minimum
0.03
1.00
22.00
0.09
38
1
0.38
18.80
0.15
2
226.00
0.00
-0.62
Std. Dev.
4.76
7.47
276.57
0.94
17933
70
0.05
1315.29
0.28
261
74.37
0.46
0.14
Observations
198
198
198
198
198
198
198
198
198
198
198
198
198
Explanation of featured variables:Expression Used in Table
Avloan/20%GDP per Capita
Age
Avloan
Avloan/GDPPc
Borr
Borr/Pers
Comp
Cost/Borr
Eff
Personnel
GDPPc
Reg
ROA
Explanation
Average loan divided by 20% of GDP per capita
Age of MFI(s)
Average Loan
Average Loan divided by GDP Per Capita
Borrowers
Borrowers per personnel
Competition
Cost per borrower
Efficiency
Personnel
GDP Per Capita
Regulation ‘Dummy’ 1 regulated; 0 otherwise
Return on Assets
50
Table 7:
Correlation Matrix of Variables used in the empirical analysis
1
1 Avloan/20%PcGDP
2
3
4
5
6
7
8
9
10
11
12
13
1
2 Age
0.06
1
3 Average Loan
0.83
0.23
1
4 Avloan/PcGDP
0.87
0.15
0.97
1
5 Borrowers
Borrowers per
6 personnel
0.01
0.39
0.15
0.11
-0.34
0.06
-0.38 -0.38
7 Competition
-0.03 -0.12 -0.01 -0.06 -0.09
8 Cost per borrower
0.19
-0.11 -0.35
0.05
1
9 Efficiency
-0.12 -0.04 -0.24 -0.25 -0.20 -0.05
0.16
0.20
1
0.38
0.31
0.19
1
0.38
1
0.14
1
10 Personnel
0.28
0.54
0.43
0.33
0.36
-0.26 -0.03
0.85
0.08
1
11 GDP Per Capita
-0.12
0.25
0.19
-0.02
0.15
0.00
0.20
0.26
-0.10
0.25
1
12 Regulation ‘Dummy’
0.41
0.16
0.53
0.50
0.25
-0.18 -0.16
0.29
-0.15
0.46
-0.03
1
13 Return on Assets
0.02
0.01
0.15
0.13
0.07
-0.14 -0.16
0.05
-0.55
0.09
0.19
0.15
1
51
Explanation of featured variables:Expression Used in Table
Avloan/20%GDP per Capita
Age
Avloan
Avloan/GDPPc
Borr
Borr/Pers
Comp
Cost/Borr
Eff
Personnel
GDPPc
Reg
ROA
Explanation
Average loan divided by 20% of GDP per capita
Age of MFI(s)
Average Loan
Average Loan divided by GDP Per Capita
Borrowers
Borrowers per personnel
Competition
Cost per borrower
Efficiency
Personnel
GDP Per Capita
Regulation ‘Dummy’ 1 regulated; 0 otherwise
Return on Assets
52
Table 8:
Selected literature utilized for theoretical framework arguments
In the consulted literature presented in a tabular form below, the effects of each variable presented will be assessed against outreach. As proxied
in the study, outreach is said to be high when the average loan is low; greater loans imply mission drift: Likewise greater outreach is positively
related client numbers. Cases where MFIs existence are said to be jeopardized, will be treated in this table as a negative effect to outreach.
Age
Christen (2001)
Olivares Polanco (2005)
Regulation
Efficiency
positive relationship
positive relationship
positive relationship
No relationship
Conning (1999)
Woller (2002)
Woller and et al (1999)
N/A
N/A
N/A
N/A
negative relationship
N/A
N/A
No relationship
No relationship
positive relationship
Implies negative
relationship
positive relationship
N/A
N/A
Mc Intosh and Bruce
(2005)
Hartaska (2004)
N/A
negative relationship
N/A
N/A
positive relationship
Positive relationship
Charitoneko and et al
(2004)
Chamlee-Wright (2005)
positive relationship
negative relationship
Competition
Positive but not significant
negative relationship in
depth
N/A
Positive relationship
No relationship
Auditing increases
breadth
Positive relationship
NA
Positive relationship
N/A
Positive relationship
53
Table 9
Panel Regression Results: lnAverage Loan Dependent Variable
Variable
C
ln (age)
ln(Comp)
ln(cost/borr)
ln(eff)
ln(pers)
Reg
ROA
**
Method:
Cross Sections Included:
Total Panel (Balanced) Observations:
Regression one
Regression two
Coefficient t-Statistic Coefficient t-Statistic
1.002
3.200
1.056
3.477
0.007
0.137
0.010
0.186
-0.886
-2.963***
-0.908
-3.017***
0.550
13.293***
0.545
13.385***
-0.871
-11.184***
-0.844
-12.460***
**
0.065
2.141
0.067
2.191**
0.038
0.423
0.038
0.423
-0.190
-0.716
-
Panel Least Squares
33
198
Regression three
Regression four
Coefficient t-Statistic Coefficient t-Statistic
0.999
3.673
0.998
3.698
0.003
0.052
-0.918
-3.181***
-0.922
-3.336***
0.552
14.766***
0.552
14.840***
-0.851
-12.963***
-0.851
-13.014***
***
0.072
2.602
0.073
3.100***
-
R2
0.728
R2
0.727
R2
0.727
R2
0.727
Adjusted R2
0.718
Adjusted R2
0.719
Adjusted R2
0.720
Adjusted R2
0.721
Significant at 5%;
***
Significant at 1%
Explanation of featured variables:Expression Used in Table
ln(Age)
ln(Avloan)
ln(Comp)
ln(Cost/Borr)
ln(Eff)
ln(Personnel)
Reg
ROA
Explanation
Age of MFI(s)
Average Loan
Competition
Cost per borrower
Efficiency
Personnel
Regulation ‘Dummy’ 1 regulated; 0 otherwise
Return on Assets
54
Table 10
Panel Regression Results: (Average Loan/GDP Per Capita) Dependent Variable
Method:
Cross Sections Included:
Total Panel (Balanced) Observations:
Variable
C
ln (age)
ln(Comp)
ln(cost/borr)
ln(eff)
ln(pers)
Reg
ROA
Regression one
Coefficient
t-Statistic
-4.590
0.005
-1.380
0.468
-0.750
0.039
0.190
-0.410
2
R
2
Adjusted R
Regression two
Coefficient
t-Statistic
-14.150
0.094
-4.428***
10.943***
-9.349***
1.242
2.030**
-1.481
-4.478
0.046
-1.272
0.477
-0.754
0.238
-0.427
0.675
0.663
R
2
Adjusted R
2
Panel Least Squares
33
198
Regression three
Coefficient
t-Statistic
-14.310
1.021
-4.243***
11.302***
-9.340***
2.781***
-1.562
-4.512
-1.343
0.494
-0.761
0.232
-0.463
0.672
0.661
R
2
Adjusted R
2
Regression four
Coefficient
t-Statistic
-14.513
-4.600***
12.483***
-9.436***
2.688***
-1.697
-4.378
-1.293
0.484
-0.693
0.231
-
0.671
0.662
R
2
Adjusted R
2
-14.494
-4.434***
12.308***
-9.819***
2.697***
0.666
0.659
**
Significant at 5%; ***Significant at 1%
Explanation of featured variables:Expression Used in Table
ln(Age)
ln(Avloan)
ln(Comp)
ln(Cost/Borr)
ln(Eff )
ln(Personnel)
Reg
ROA
Explanation
Age of MFI(s)
Average Loan
Competition
Cost per borrower
Efficiency
Personnel
Regulation ‘Dummy’ 1 regulated; 0 otherwise
Return on Assets
55
Table 11:
Panel Regression Results: (Average Loan/20% of GDP per Capita) Dependent Variable
Method:
Cross Sections Included:
Total Panel (Balanced) Observations:
Variable
C
ln (age)
ln(Comp)
ln(cost/borr)
ln(eff)
ln(pers)
Reg
ROA
Regression one
Coefficient t-Statistic
-4.005
-0.578
-3.291
0.486
-0.394
0.149
0.369
-3.526
2
R
2
Adjusted R
Regression two
Coefficient t-Statistic
-5.317
-4.406***
-4.547***
4.887***
-2.104**
2.033**
1.692*
-5.530***
-4.549
-0.650
-3.600
0.551
-0.460
0.200
-3.526
0.360
0.336
R
2
Adjusted R
2
Panel Least Squares
33
198
Regression three
Coefficient t-Statistic
-6.647
-5.205***
-5.113***
5.980***
-2.498**
2.979***
-5.503***
-4.121
-2.558
0.514
-0.473
0.019
-3.289
0.350
0.330
R
2
Adjusted R
2
Regression four
Coefficient t-Statistic
-5.691
-3.556***
5.248***
-2.408**
0.303
-4.829***
-4.108
-2.545
0.529
-0.479
-3.314
0.258
0.238
R
2
Adjusted R
2
-5.696
-3.553***
6.254***
-2.462**
-4.914***
0.257
0.242
*
Significant at 10%; **Significant at 5%; ***Significant at 1%
Explanation of featured variables:Expression Used in Table
ln(Age )
ln(Avloan)
ln(Comp)
ln(Cost/Borr)
ln(Eff)
ln(Personnel)
Reg
ROA
Explanation
Age of MFI(s)
Average Loan
Competition
Cost per borrower
Efficiency
Personnel
Regulation ‘Dummy’ 1 regulated; 0 otherwise
Return on Assets
56
Table 12
Panel Regression Results: (Average Loan/Synthetic GDP) Dependent Variable
Method:
Cross Sections Included:
Total Panel (Balanced) Observations:
Variable
C
ln (age)
ln(Comp)
ln(cost/borr)
ln(eff)
ln(pers)
Reg
ROA
Regression one
Coefficient
t-Statistic
-5.5770
0.0075
-0.90811
0.5499
-0.8715
0.0654
0.0383
-0.1897
Regression two
Coefficient
t-Statistic
-17.8068
0.1368
-3.0171***
13.2933***
-11.1836***
2.1407**
0.4227
-0.7156
-5.5873
-0.9222
0.5510
-0.8721
0.0678
0.0343
-0.1922
0.7280
0.7179
R
2
Adjusted R
2
R
2
Adjusted R
2
Panel Least Squares
33
198
Regression three
Coefficient
t-Statistic
-18.4274
-3.2683***
13.6076***
-11.2369***
2.7338***
0.4009
-0.7282
0.9457
-0.94018
0.556632
-0.8783
0.0707
-0.1897
0.7280
0.7194
R
2
Adjusted R
2
Regression four
Coefficient
t-Statistic
3.3802
-3.38253***
14.70651***
-11.5788***
2.9856***
-0.7207
-5.58126
-0.92235
0.551688
-0.8507
0.0728
-
0.7277
0.7206
R
2
Adjusted R
2
-20.6801
-3.3358***
14.8398***
-13.0142***
3.1004***
0.7270
0.7213
*
Significant at 10%; **Significant at 5%; ***Significant at 1%
Explanation of featured variables:Expression Used in Table
ln(Age)
ln(Avloan)
ln(Comp)
ln(Cost/Borr)
ln(Eff)
ln(Personnel)
Reg
ROA
Explanation
Age of MFI(s)
Average Loan
Competition
Cost per borrower
Efficiency
Personnel
Regulation ‘Dummy’ 1 regulated; 0 otherwise
Return on Assets
57
Table 13
Panel Regression Results: Dollar Years Dependent Variable
Method:
Cross Sections Included:
Total Panel (Balanced) Observations:
Regression one
Coefficient
t-Statistic
Variable
C
ln (age)
ln(Comp)
ln(cost/borr)
ln(eff)
ln(pers)
Reg
ROA
1.817
-0.009
-0.917
0.583
-0.862
0.017
0.145
-0.361
2
R
2
Adjusted R
Regression two
Coefficient
t-Statistic
4.507
-0.132
-2.368**
10.953***
-8.590***
0.439
1.246
-1.057
1.830
-0.900
0.582
-0.861
0.014
0.150
-0.358
0.620
0.606
R
2
Adjusted R
2
Panel Least Squares
33
198
Regression three
Coefficient
t-Statistic
4.689
-2.478**
11.164***
-8.61***8
0.446
1.364
-1.053
1.858
-0.883
0.590
-0.863
0.165
-0.376
0.620
0.608
R
2
Adjusted R
2
Regression four
Coefficient
t-Statistic
4.834
-2.450**
12.096***
-8.664***
1.565
-1.119
1.646
-0.960
0.628
-0.898
-0.383
0.620
0.610
R
2
Adjusted R
2
Regression five
Coefficient
t-Statistic
4.559
-2.678***
14.843***
-9.215***
-1.135
1.756
-0.921
0.622
-0.843
-
0.615
0.607
R
2
Adjusted R
5.046
-2.578
14.819***
-9.972***
-
2
0.613
0.607
**
Significant at 5%; ***Significant at 1%
Explanation of featured variables:Expression Used in Table
ln(Age)
ln(Avloan)
ln(Comp)
ln(Cost/Borr)
ln(Eff)
ln(Personnel)
Reg
ROA
Explanation
Age of MFI(s)
Average Loan
Competition
Cost per borrower
Efficiency
Personnel
Regulation ‘Dummy’ 1 regulated; 0 otherwise
Return on Assets
58
Table 14
Panel Regression Results: (Dollar Years/GDP Per Capita) Dependent Variable
Method:
Cross Sections Included:
Total Panel (Balanced) Observations:
Regression one
Coefficient t-Statistic
Variable
C
ln (age)
ln(Comp)
ln(cost/borr)
ln(eff)
ln(pers)
Reg
ROA
6.675
-0.103
-0.720
-0.405
-1.023
1.217
-0.398
-0.462
2
R
2
Adjusted R
Regression two
Coefficient t-Statistic
12.547
-1.126
-8.133***
-0.791
-7.400***
23.223***
-2.676***
-1.041
6.804
-0.734
-0.224
-1.031
1.183
-0.347
-0.439
0.821
0.815
R
2
Adjusted R
2
Panel Least Squares
33
198
Regression three
Coefficient t-Statistic
13.087
-8.371***
-0.460
-7.460***
27.659***
-2.449**
-0.989
6.966
-0.728
-1.029
1.179
-0.340
-0.425
0.820
0.814
R
2
Adjusted R
2
Regression four
Coefficient t-Statistic
18.283
-8.425***
-7.465***
28.015***
-2.417**
-0.960
7.037
-0.735
-0.976
1.184
-0.349
-
0.820
0.815
R
2
Adjusted R
2
18.820
-8.547***
-7.736***
28.295***
-2.491**
0.819
0.815
**
Significant at 5%; ***Significant at 1%
Explanation of featured variables:Expression Used in Table
ln(Age )
ln(Avloan)
ln(Comp)
ln(Cost/Borr)
ln(Eff)
ln(Personnel)
Reg
ROA
Explanation
Age of MFI(s)
Average Loan
Competition
Cost per borrower
Efficiency
Personnel
Regulation ‘Dummy’ 1 regulated; 0 otherwise
Return on Assets
59
Table 15
Panel Regression Results: lnBorrowers Dependent Variable
Method:
Cross Sections Included:
Total Panel (Balanced) Observations:
Regression one
Coefficient t-Statistic
Variable
C
LOG(AGE)
LOG(AVL)
LOG(COMP)
LOG(EFF)
LOG(PERS)
REG
ROA
6.675218
-0.1033
-0.71997
-0.40501
-1.02312
1.216826
-0.39783
-0.46243
2
R
2
Adjusted R
Regression two
Coefficient t-Statistic
12.547***
-1.126
-8.133***
-0.791
-7.400***
23.223***
-2.676***
-1.041
6.804078
-0.73411
-0.22375
-1.03091
1.182667
-0.34737
-0.43911
0.821
0.815
R
2
Adjusted R
2
Panel Least Squares
33
198
Regression three
Coefficient t-Statistic
13.087***
-8.371***
-0.460
-7.460***
27.659***
-2.449**
-0.989
6.966427
-0.72758
-1.02896
1.179462
-0.33988
-0.4247
0.820
0.814
R
2
Adjusted R
2
Regression four
Coefficient t-Statistic
18.283***
-8.425***
-7.465***
28.015***
-2.417**
-0.960
7.036671
-0.73499
-0.97556
1.183881
-0.3494
-
0.820
0.815
R
2
Adjusted R
2
18.820***
-8.547***
-7.736***
28.295***
-2.491**
0.819
0.815
**
Significant at 5%; ***Significant at 1%
Explanation of featured variables:Expression Used in Table
ln(Age)
ln(Avloan)
ln(Comp)
ln(Eff)
ln(Personnel)
Reg
ROA
Explanation
Age of MFI(s)
Average Loan
Competition
Efficiency
Personnel
Regulation ‘Dummy’ 1 regulated; 0 otherwise
Return on Assets
60
List of acronyms.
BRI
Bank Rakyat Indonesia
CGAP
Consultative Group to Assist the Poorest
EA
East Africa
EAC
East African Community
GDP
Gross Domestic Product (Per Capita)
LDCs
Least Developed Country/ies
MFI
Microfinance Institution/s
MIMAS
Manchester Information and Associated Services
MIX
Microfinance Information eXchange
REPOA
Research on Poverty Alleviation
ROA
Return on Assets
ROCE
Return on Capital Employed
SDI
Subsidy Dependence Index
SDR
Subsidy Dependence Ratio
SPM
Subsidy Per Member
UNCTAD
United Nations Conference on Trade and
Development
WDI
World Development Indicators.
61