Should microfinance institutions specialize
in financial services?
Robert Lensink
Faculty of Economics and Business, University of Groningen, Development
Economics Group, Wageningen University, The Netherlands.
E-mail: B.W.Lensink@rug.nl
Roy Mersland
Faculty of Economics and Social Sciences, School of Management,
University of Agder, Kristiansand, Norway
Vu Thi Hong Nhung
Faculty of Economics and Business, University of Groningen
Email: t.h.n.vu@rug.nl
1
Abstract
Using a global data set of microfinance institutions (MFIs) in 61 countries, this study
tests whether those that specialize in financial services perform better in terms of
financial returns and/or outreach than those that provide both financial and
nonfinancial (i.e., business development and social) services. The results suggest that
MFIs that provide social services perform better in terms of reaching out to poorer
customers but worse in their financial results. With regard to business development
service providers, their performance is similar to that of MFIs that specialize in
financial services.
Keynotes: Microfinance; Business development services; Outreach; Financial
sustainability; Random effects regressions.
JEL codes: G21; O16; C23.
2
The impact of microfinance, defined as the provision of financial services to poor
populations, has become a hotly contested subject. It enjoys widespread appeal as an
antipoverty tool; however, many questions regarding its actual influence remain
unanswered (Hermes and Lensink, 2007). Key controversies relate to whether
suppliers of microfinance should follow a minimalist approach, providing
microfinance only, or should provide microfinance alongside other important social
services, in a sort of “microfinance-plus” (Bhatt and Tang, 2001; Morduch, 2000).
Initially, microfinance institutions (MFIs) focused on providing small loans and
microcredit. The industry soon started to recognize though that the poor needed a
wide variety of financial products to improve their lives, such that microcredit
evolved into microfinance. The term refers to a broad set of financial services,
including loans, savings, insurance, and transfer services, as well as remittances
aimed at low income clients. Conventional wisdom suggests that poor households
benefit from a combination of these services, rather than just the provision of credit
(Aghion and Morduch, 2005). Some MFIs thus began to broaden their activities even
further, providing financial services as well as business training, health care, and
social services. These so-called “plus” activities acknowledge that though financial
services are critical to microfinance, they address only one of the many problems of
the poor. For example, the poor have comparatively high disease rates, and few know
how to use borrowed funds efficiently. In such conditions, microcredit is insufficient.
Most studies in this field analyze the trade-off between serving the poor and financial
sustainability (Cull et al., 2007; Hermes et al., forthcoming). To the best of our
knowledge, the question of whether specializing in financial services or integrating
3
financial services with nonfinancial services is better for financial sustainability has
not been tested empirically. We take up the challenge by comparing microfinanceplus providers, which offer nonfinancial services, and specialized MFIs, which focus
only on financial services, according to their financial results and outreach to the
poor. As an added contribution, we compare financial performance and outreach
across two types of plus services: business development services (BDS) and social
services.
The remainder of this article proceeds as follows: We outline the concept of
microfinance-plus in the next section, followed by our conceptual framework of the
impact of these plus services. From our empirical literature review, we derive some
hypotheses; we then describe our data and methodology for testing these hypotheses.
Finally, we present estimates regarding financial results and outreach and conclude
with a discussion of our findings.
What Is Microfinance-Plus?
Microfinance-plus refers to the provision of developmental services to customers,
such as training or health services, alongside financial services. An overall
understanding of the concept is clear and relatively simple; a more detailed
assessment also is possible. For example, a MFI providing savings, insurance, or
money transfers alongside loans is not involved in microfinance-plus, because all its
services are financial in nature. Moreover, a MFI that provides informational sessions
to potential customers or trains existing customers in the use of credit or the
importance of repayment is not practicing microfinance-plus, nor is a MFI that
partners with another organization that provides customers with plus services. We
4
define a plus service specifically as a nonfinancial service provided by the MFI itself.
Various MFIs have taken on a wide variety of plus services. Maes and Foose (2006)
report plus services ranging from access to markets and business development
services to health provision and literacy training. Generally though, plus services
belong to either BDS or social services. Business development services usually aim to
boost competitiveness through higher productivity, better product design, improved
service delivery, or enhanced market access (Sievers and Vandenberg, 2007). They
comprise a broad range of nonfinancial services, including management or vocational
skills training; marketing and technical assistance; technology access; productivity
and product design; accounting and legal services; and access to various information
about standards, regulations, or ideas in an enterprise field. In contrast, social services
integrate credit with health, education, or other programs intended to raise health
consciousness, health practices, and the use of formal healthcare.
A MFI can offer plus services in parallel or unified form. A parallel service is offered
by specialized personnel, whereas a unified service is offered by the same
microfinance staff, normally credit officers (Dunford, 2002). The plus service of
unified providers tends to be limited to training, whereas parallel providers can offer a
wider spectrum of services by relying on staff members with various specializations
(Dunford, 2002). The advantage of unified services is that additional costs should be
minimal (Vor der Bruegge et al. 1999), such that the services can be funded mainly
by the customers themselves through loan interest payments. Parallel services instead
tend to be funded by special donations or customer service charges. Some MFIs also
cross-subsidize their parallel services.
5
Conceptual Framework of the Impact of Microfinance-Plus
A clear-cut, unambiguous theory about the impact of microfinance-plus activities on
financial performance and/or outreach is not available. However, using different
theories from extant literature, we can derive a framework to assess the impact of
microfinance-plus, as we illustrate in Table 1. We classify impact according to its
relationship with identified goals, such as expected or unexpected objectives, and the
causation types, whether direct or indirect.
First, microfinance-plus can exert a direct, anticipated impact (Quadrant I) if it solves
multidimensional poverty problems. Many practitioners and researchers argue that the
poor need more than financial services to improve their lives (Dunford, 2002;
Khandker, 2005), because poverty is not due just to a lack of funds but also relates to
vulnerability, powerless, and dependency (Bhatt, 1998). Economic stress events, such
as the birth of child, marriage of family members, severe illness, death of a major
breadwinner, or natural disasters, strain household income and resources, which in
turn influences the likelihood of timely loan repayments (Noponen and Kantor,
2004). To overcome poverty, the poor usually require access to a coordinated
combination of microfinance and other development services (Khandker, 2005;
Mosley and Hulme, 1998). Thus microfinance-plus providers should have superior
effects in alleviating poverty. Second, plus activities, especially training, can improve
microfinance customers’ human capital. In specialized MFIs, borrowers are mainly
traders who participate in simple production and service provision (Dawson, 1997).
Clients may plan to use loans efficiently, but their attempts may be limited by their
lack of or narrow knowledge. Many borrowers never go beyond traditional food
processing, handicrafts, or petty trade (Dawson, 1997). Without sufficient cognitive
6
skills, micro-entrepreneurs even might earn a negative return on their capital (de Mel
et al., 2008). We argue that improved human capital could enable microfinance
clients to service bigger loans, which would enhance the financial performance of
MFIs though economies of scale. Third, microfinance-plus may help reduce the risk
of default. Training should reduce credit risks that arise when borrowers use loans for
consumption rather than production activities (Marconi and Mosley, 2006). Plus
activities also may improve customer satisfaction and thus increase repayment and
retention rates. Fourth, MFIs may obtain a comparative advantage if they provide a
range of integrated services (Khandker, 2005; Mosley and Hulme, 1998). With
integrated services, MFIs can better differentiate their products and attract new
customers. Fifth and finally, plus services may help MFIs achieve sustainability by
providing much needed services to borrowers and encouraging their trust. Borrowers
may be more confident about depositing their saving and motivated to achieve timely
repayment to keep receiving continuously larger loans.
However, microfinance-plus also may create direct, unexpected effects (Quadrant II),
including higher operational costs. A study of four Freedom from Hunger affiliates
reveals that the direct cost of including learning sessions related to family, health,
nutrition, business development, and self-confidence added up to between 4.7 and 10
percent of each MFI’s operational costs (Vor der Bruegge et al., 1999). In addition,
integrated plus services may increase administrative burdens. The provision of
training and technical assistance may distract MFIs from their credit administration
and decrease repayment rates (Berger, 1989). Furthermore, many microfinance
borrowers do not consider training useful and do not retain or apply the acquired
knowledge, whether due to the poor quality of the offered services or irrelevant
7
training provided. They consider time spent in training an opportunity cost for credit
(Goldmark, 2006). Consequently, the adding cost of training does not provide
immediate and tangible benefits, which may reduce credit demand (Berger, 1989).
Microfinance-plus also can create indirect effects in the community (Quadrant III),
such as improved productivity, employment, and sustained growth (Dawson, 1997). It
would be hard to generate ongoing increases in employment, technical sophistication,
and output if MFIs specialize only in financial services (Hulme and Mosley, 1996).
Finally, some indirect, unexpected impacts (Quadrant IV) may be due to the difficulty
of evaluating performance, because MFIs need a clear, concise measure of good
performance. Performance by MFIs that provide microfinance-plus services is more
difficult to measure and requires more time to verify (Tendler, 1989). Some MFIs
even offer plus services to distract attention away from their inefficient microfinance
services (Berenbach and Guzman, 1994).
<insert Table 1>
Empirical Literature Review and Hypotheses
This study aims to answer three research questions:
1. Do specialist MFIs achieve better financial results than microfinance-plus
providers?
2. Are microfinance-plus providers able to reach out to poorer customers than
their specialist peers?
3. What types of plus services are most effective for MFIs in terms of financial
performance and outreach?
8
Relationship of Financial Performance, Types of MFIs, and Plus Services
Many policy makers argue that the only way for MFIs to become self-sufficient,
obtain sustainability, and reach optimal scale is to concentrate on financial services
(Dunford, 2002; Otero, 1994). However, previous studies also confirm that BDS
make substantial, positive contributions to profits for not only microcredit users but
also BDS providers and MFIs in general. These results may relate to the quality and
type of BDS, which can be improved by tending to the specifics, focusing on
vocational skills training and market access instead of traditional management
training (Sievers and Vandenberg, 2007). One study, conducted in Sarvodaya’s Rural
Enterprise Development Service in Sri Lanka, reveals that adding technical assistance
to a standard microcredit program can help increase both loan disbursement and
repayment rates (Dawson, 1997). Another study focused on 283 participants of a
microfinance program in Zambia suggests that training positively affects the growth
of enterprise profits (Copestake et al., 2001).
Research using data from 1,798 households across 87 villages in Bangladesh during
1991–1992 investigates credit and noncredit effects specifically (McKernan, 2002).
The focal noncredit services include social development workshops, a set of pledges,
and specialized skills training (i.e., BDS), as well as health advice (drink boiled
water), family advice (educate children), and household management strategies (save
money in the bank, plant vegetable gardens) (i.e., social services). The author finds
large positive effects of noncredit programs on self-employment profits but does not
distinguish between different noncredit aspects.
9
A study of the Bangladesh Rural Advancement Committee shows that beyond the
benefits of microcredit, female clients who receive skill training obtain significantly
higher income than those who do not. Moreover, the BDS package is highly cost
effective for clients, because the added gains to their new business exceed the service
charge many times over. However, the fees charged for services cover only 47
percent of its total cost (Halder, 2003). A more recent study also assessed the impact
of incorporating BDS, in the form of entrepreneurial training, into a microfinance
program by using a randomized control trial (Karlan and Valdivia, forthcoming).
They measure the marginal impact of adding business training to a Peruvian group
lending program for female borrowers; for borrowers, the treatment leads to limited
improvements in business knowledge, practices, and revenues. For the MFI though,
the program increases client retention rates.
Previous studies also have considered the impact of integrating social services with
credit. Dunford (2002) provides expansive evidence of the combined effects of
financial and education services, especially health and nutrition training, in “Credit
with Education” programs in Ecuador, Honduras, Burkina Faso, Thailand, and other
locations. Credit with Education helps borrowers increase their income, enhance their
household’s ability to reduce risk, and deal with crises and economic challenges,
leading to positive changes in clients’ health knowledge and better dietary quality of
the food given to participants’ children. Financial and education services also have
positive impacts on women’s self-confidence and status in the community. Some
Credit with Education providers, such as CRECER in Bolivia or Lower Pra Rural
Bank in Ghana, obtain efficiency and sustainability ratios of 106 and 130 percent,
respectively. However, these studies also offer clear evidence that education increases
10
the costs of village banking. The thee-year average additional cost of extra education
ranges from 5.9 percent in Bolivia to 9.6 percent in Burkina Faso. Credit with
Education providers maintain though that the extra satisfaction and outcomes
experienced by their clients, their families, and their communities justify this
additional cost of education.
Evidence from the Self-Employed Women’s Association, a development organization
in India, shows that health services training can help prevent illness, which also
reduces illness-related borrowing or drift business loans to pay medical expenses. The
findings support arguments for the effectiveness of microfinance-plus rather than
specialized credit programs to improve self-employed women’s productivity and
enhance their social security (Noponen and Kantor, 2004).
Another study of the impact of credit and nonfinancial services, defined as health and
education, in Honduras and Ecuador shows that health bank participation
significantly raises subsequent healthcare compared with participation in credit only.
This plus service helps reduce the tendency to switch from breast-feeding to bottlefeeding when income increases. However, the study finds no clear link between
health programs and bank performance (Smith, 2002).
This existing literature implies then that adding plus services increases operating
expenses for providers, but the synergies that result from linking effective services
may help them compensate for these extra costs, such that financial performance may
even increase. Regarding the comparison of BDS and social services, BDS training
benefits likely exceed the service cost. We thus hypothesize that specialist MFIs
11
obtain better financial results than their plus peers. However, BDS providers should
be able to cover their service costs better and obtain better financial performance than
those that focus on social services.
Relationship of Outreach, Types of MFIs, and Plus Services
Although MFIs aim to reach poor people, most of them access the “upper poor” much
better than the “very poor.” Thus microfinance offers an effective means of reducing
poverty but may not influence extreme poverty (Mosley, 2001). Evidence from
Bolivia shows that MFIs effectively reach the poor but not the poorest. A major
argument in support of microfinance-plus is that it enables MFIs to reach poorer and
more vulnerable customers (Halder, 2003; Maes and Foose, 2006). That is, other
antipoverty modalities, including primary health, primary education, and agricultural
extensions, are necessary to reach the poorest sectors (Mosley, 2001). In addition,
microfinance research generally measures customers’ levels of vulnerability by their
average loan size and the fraction of female customers (Bhatt and Tang, 2001).
Women appear to accept nonfinancial services more readily and also need them more
(Aghion and Morduch, 2005). We therefore expect to find that MFIs practicing
microfinance-plus, especially those focusing on social services, exhibit smaller
average loan sizes and serve relatively more female clients than specialized MFIs.
Data and Estimation Methodology
We use observations of 290 rated MFIs from 61 countries. Third-party specialized
rating agencies perform these assessments. Submitting to a rating can grant a MFI
access to external funding from investors. Because the assessment reports are
undertaken by third parties, they often cover a wide range of organizational features,
along with financial data and social and financial indicators. Each rating relies on four
12
years of data, and they span the period 2001 to 2007, which means our data cover
1998 to 2007. Most data are from 2001–2005.
No data set can be perfectly representative of the microfinance field; ours contains
relatively fewer mega-sized MFIs and does not cover the virtually endless number of
small savings and credit cooperatives. The former are rated by such agencies as
Moody’s and Standard & Poor’s; the latter are not rated. However, our use of rating
reports should be relevant for studying the effects of microfinance-plus, because
MFIs that are rated have a common interest in accessing funding and increasing their
sustainability.i The data set includes both specialists and providers of plus services, so
it enables meaningful comparisons.
Although we controlled for inflation by including inflation rates in our model, the
different inflation rates across 61 countries make comparisons difficult. To solve this
problem, we converted the monetary variables into U.S. dollar (USD) amounts at the
going exchange rate. According to the purchasing power parity theorem of
international finance (Solnik and McLeavey, 2003), conversion into USD implies that
local inflation has been taken into account.
We also assume that the rating agency has made the necessary corrections to the
financial reports to enable a reasonable comparison of MFIs. This assumption is in
line with the benchmarking objective outlined by the Rating Fund,ii which funds the
rating reports that constitute our data set.
Estimation Methodology
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The general specification of the financial performance and outreach equations we
estimate is as follows:
yijt   0  1MF _ PLijt   M jt   MFijt  ci   ijt ,
where the dependent variable yijt is a measure of financial performance and outreach
of the ith MFI located in country j at time t; β0 is a constant term; MF_PLijt is the key
explanatory variable that denotes whether the ith MFI provides plus services in
country j at time t; Mjt is a vector of control variables describing the macroeconomic
environment in country j at time t; MFjt is a vector of control variables describing the
features of the ith MFI in county j at time t; ci is the MFI’s individual unobserved
effects; and εijt is a random error term.
For our empirical analysis, we use panel data. The main explanatory variable of
interest, MF_PLijt is time invariant, so a fixed-effects model is not appropriate; we use
a random effects model instead. This model assumes that all explanatory variables
( MF _ PLijt , M jt , MFijt ) are strictly exogenous; that E (uit xi1, ..., xit , ci )  0 ; and that all
explanatory variables are uncorrelated with the MFI heterogeneity term ci , that is,
E (ci xi1, ..., xit )  0 . However, random effects models cannot control for selection bias
due to selection on unobservable variables. We lack reliable instruments for the
choice of MFI type, so we do not attempt to address selection bias formally with an
instrumental variable technique. Instead, we compare the estimates with and without
controls to determine the extent to which it is likely that the result disappears with the
inclusion of unobserved variables (see Altonji et al. 2005; Bellows and Miguel 2009).
Hence, we follow the spirit of the method proposed by Altonji et al (2005). This
14
approach provides an indication of how sensitive ordinary least squares regressions
are to any selection bias due to unobservables.
We next provide some descriptive statistics of the dependent variables and the
explanatory variables. In Appendices 1–3, we also offer definitions, sources, a list of
countries, and a correlation matrix of the dependent and independent variables.
Dependent Variables
In general, a MFI has a dual objective: achieve self-sufficiency by covering its costs
and reach many poor clients (outreach). Following Cull et al. (2007), we use the
financial self-sufficiency ratio (FINSS) as a main indicator of financial performance.
This ratio demonstrates the ability of MFIs to be fully sustainable in the long run by
covering all their operating costs and maintaining the value of their capital. The
FINSS ratio is a better measure of financial performance than standard financial
ratios, such as return on assets or equity, because it can adjust the data and involves a
more complete list of inputs and outputs. As a robustness check, we also include
operational self-sufficiency (OPERSS) and return on assets (ROA) measures; we
expect better financial performance to be associated with higher OPERSS, FINSS, and
ROA. Finally, we consider the effect on the portfolio at risk (PAR), for a period of 30
days. Better financial performance is related to a decline in PAR.
The outreach measure includes average loan size (AVLOAN) and share of female
borrowers (PERCWOMEN), which are proxies for the depth of outreach used widely
in prior microfinance literature (Ahlin et al., forthcoming; Cull et al., 2007, 2009;
Hermes et al., forthcoming; Olivares-Polanco, 2005; Schreiner, 2002). Average loan
15
size is a proxy for the poverty of borrowers, such that smaller loans roughly imply
more depth of outreach, because MFIs appear to be providing more loans to the very
poor. A higher percentage of female borrowers also indicates more depth of outreach,
because lending to women generally is related lending to the poor. We expect that for
better outreach, the average loan size coefficient will be significantly negative and the
percentage of women measure will be significantly positive.
Explanatory Variables
In the first step of the analysis, we examine the impact of plus services in general,
using as our main explanatory variable MFI_PL. This binary dummy variable equals
1 if the MFI is a plus provider that integrates BDS or social services along with
financial services and 0 if it is a specialized MFI. Next, we differentiate the different
forms of plus services with three binary dummy variables:
(1) MF_FIN equals 1 if the MFI is a specialist and 0 if it is a plus provider of
BDS training or social services.
(2) MF_BDS equals to 1 if the MFI is a plus provider that integrates BDS and 0 if
it is a specialist or plus provider that integrates social services.
(3) MF_SOCIAL equals 1 if the MFI is a plus provider that integrates social
services and 0 if it is a specialist or plus provider that integrates BDS training.
For control variables related to the macroeconomic environment, we include gross
domestic product (GDP, in constant 2000 USD), the Human Development Index
(HDI), and inflation. Both GDP and HDI proxy for the overall level of development.
The HDI includes measures of health, literacy, and education, as well as per capita
income. By controlling for GDP and HDI, we capture institutional differences
16
(Claessens et al., 2001; Lensink and Hermes, 2004; Mersland and Strom, 2009). We
include inflation to control for macroeconomic conditions, because all programs
tolerate the cost of inflation. Microfinance programs also have a better chance of
achieving self-sufficiency if they operate in countries where inflation is controlled
and moderate (Rhyne and Otero, 1992).
Finally, we include total operating expenses (OPEREXP), number of credit clients per
loan officer (CREDOPROD), whether the MFI offers voluntary savings to clients
(DMVOLS), the debt/equity ratio (DEBTED), and AGE and AGE2 to control for MFI
characteristics. We provide these values in Table 2.
<insert Table 2>
In Table 3, we also provide general descriptive statistics regarding the relations of
different types of plus providers and specialists, as well as between financial
performance and outreach. The table clearly suggests that plus providers that combine
BDS with financial services perform better financially than their peers that integrate
social with financial services; they even perform more effectively than specialized
MFIs. In particular, the mean values for FINSS, OPERSS, and ROA are higher for
plus providers with BDS than for plus providers with social services or specialists.
Moreover, plus providers seem to achieve better outreach. The mean value of
AVLOAN is lower when MFIs provide additional BDS and social services than when
they specialize in financial services. This finding indicates that plus providers,
regardless of the type of services, focus more on poor borrowers, whose average loan
17
sizes are much lower than those of wealthier borrowers. Plus providers that include
BDS reach the very poorest to the greatest extent by providing smaller loans than
their plus peers with social services. In addition, plus providers, whether with BDS or
social services, perform better in terms of reaching female borrowers than specialists.
Whereas specialists exhibit 70 percent female borrowers, these values are 76 percent
for BDS and 91 percent for social services plus providers.
<insert Table 3>
Empirical Results
Financial Performance
Models 1 and 3 in Table 4 suggest that more specialized MFIs perform better
financially. However, this relationship may suffer from a selection problem, for
which the random effects model cannot control. The random effects model may
provide biased results and overestimate the true impact since it assumes that the
unobservables affecting outcomes are uncorrelated with our variable of interest. Thus,
the positive financial performance of MFIs that specialize in financial services may
reflect the correlations between their specialization and unobserved variables that
affect financial performance. Based on the spirit of the approach by Altonji et al.
(2005), Bellows and Miguel (2009) propose a very simple method that can be used to
test how sensitive OLS regressions are to any selection bias based on unobservables.
They argue that by adding further control variables, we can examine if our main
result disappears when we include these unobserved variables that both explain
financial performance and correlate with specialization. That is, if adding controls
18
attenuates the coefficient for the MFI specialization dummy, our results likely suffer
from an omitted variable bias. Specifically, we compare the coefficient of the
variable of interest in a model without controls with the coefficient in a model with a
full set of controls and thereby calculate the extent to which adding controls
attenuates its magnitude. With the assumption that we can proxy for the possible bias
due to selection on unobservable variables with the bias due to selection on
observable variables, we estimate how large the former would need to be to explain
away the entire effect of MFI specialization on financial performance (see also
Altonji et al. 2005; Bellows and Miguel 2009). Models 2 and 4 contain the results
when we add controls: The significant impact of MFI specialization disappears.
However, a significant effect on PAR remains. Adding controls does not reduce the
impact of MFI specialization on PAR, and the effect even becomes bigger. Adding
nine observed variables to the unconstrained model increases the coefficient from
0.015 to 0.026. It follows that it seems highly likely that adding unobserved variables
could not explain away the effect of MFI specialization on PAR.
< Insert Table 4>
We also distinguish the three different types of MFI services: (1) only specialized
financial services, (2) financial services and BDS, and (3) financial services and
social services. We include the mf_social and mf_bds dummies, as well as a constant.
This specification implies that the constant measures the impact of MFIs that
specialize; the impact of MFIs that also provide social services equals the sum of the
constant and mf_social; and the impact of MFIs that also provide BDS equals the sum
of the constant and mf_bds. Significant values of mf_bds or mf_social imply that the
impact of plus providers differs from that of MFIs that specialize in financial services.
19
The results in Table 5 indicate that BDS have a positive effect on MFIs’ financial
performance. The positive coefficient for MF_BDS is not significant though, so their
financial performance does not differ from that of MFIs that specialize in financial
services. The negative, significant coefficient of MF_SOCIAL confirms that MFIs
that provide social services, such as health services, literacy, and nutrition training,
perform worse financially than do specialized MFIs. Adding the controls does not
have a notable impact on the coefficient; following the reasoning suggested by
Altonji et al. (2005), the unobserved variables appear unlikely to explain away the
effect.
Social services also may impose additional costs on clients without ensuring clear
financial benefits for MFIs. That is, health or nutrition training might not provide
clients immediate and tangible benefits that help them increase their income. Our
results confirm that providing social services significantly decreases the selfsustainability and profits of MFIs. Providing social services may have a sizeable
social impact, but in terms of self-sustainability and profits, MFIs that use Credit with
Education can hinder the drive toward sustainability (Dunford, 2002).
< Insert Table 5>
Outreach
We have hypothesized that higher values for AVLOAN are associated with lending to
wealthier people and hence lower outreach. In contrast, an increase in PERCWOMAN
implies better outreach. Similar to our estimates of financial performance, we use a
random effects panel model. We have only one observation per MFI for the
20
percentage of female borrowers and thus cannot use panel estimators to explain
PERCWOMAN. Instead, we use simple ordinary least squares and provide the results
in Tables 6 and 7.
< Insert Table 6>
According to the results in Table 6, MFIs with plus activities tend to focus more on
the poor and female borrowers. In all regressions for LNAVLOAN, MF_FIN is
significantly negative, but it is positive for the regressions with PERCWOMAN.
These results remain stable, with the same signs and significant effects, when we add
the control variables. Because the coefficient does not substantially change with the
inclusion of the controls, it is unlikely that the results would disappear were we to
include unobserved variables to control for possible selection bias.
Regarding the effects of different types of plus activities, Table 7 suggests that both
types of plus providers focus more on the poor; the coefficients of MF_BDS and
MF_SOCIAL are negative in the models explaining AVLOAN and positive in the
regressions with PERCWOMAN. However, this effect appears significant only in the
case that MFIs provide social services to borrowers.
< Insert Table 7>
Conclusions
We investigate whether MFIs that specialize in financial services perform differently
financially or in terms of outreach than do MFIs that also provide BDS and/or social
services. Using a large data set, we find that MFIs that provide social services in
addition to financial services perform worse financially but better in terms of reaching
21
out to the poor. Yet we do not find any significant difference in performance between
MFIs that specialize and those that also provide BDS—in contrast with conventional
wisdom that implies nonfinancial services drag down sustainability efforts. Providing
BDS services does not hurt financial self-sustainability. Moreover, even though social
service provision imposts costs, it also offers a clear gain in terms of better outreach
to the poor.
Our study cannot offer a decisive conclusion about the preferred type of MFI, nor do
we argue conclusively whether MFIs should specialize or not. Rather, our findings
suggest that different approaches have different advantages. If a MFI is mainly
interested in reaching the poor, it should provide social services, in addition to
financial services. However, this choice will impose the cost of lower financial
performance.
Furthermore, our study represents a first attempt to understand the effects of different
types of microfinance services on financial performance and outreach. The
importance of including nonfinancial services demands continued research efforts. Of
particular interest would be an investigation of how “smart” subsidies might help
sponsor the additional costs of providing plus services, as well as how coordinated
nonfinancial services provided by non-MFIs, in cooperation with the MFIs, might
influence MFI performance. Additional rigorous studies about whether different plus
services actually enhance customer impacts also are needed.
22
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26
Appendix 1: Variable description
Variables
Description
Sources
FINSS
Adjusted operating revenue / Adjusted (
Authors’ calculation1
financial expense + loan loss provision expense
+ operating expense
OPERSS
Operating revenue / ( Financial expense + loan
Authors’ calculation
loss provision expense + operating expense
ROA
Net operating income / average total assets
PAR30
Portfolio at risk (30 days)
LNAVLOAN
Log(Total value of loans / number of credit
Authors’ calculation
Authors’ calculation
clients)
PERCWOMAN
Percentage of female borrowers
Authors’ calculation
MF_BDS
if MFI provides BDS 1, otherwise 0
Authors’ calculation
MF_SOCIAL
if MFI provides social services 1, otherwise 0
Authors’ calculation
MF_FIN
If MFI provides only financial services,
Authors’ calculation
otherwise 0
MF_PL
if MFI is plus provider 1, specialist 0
Authors’ calculation
OPEREXP
Total operating expenses at the end of given
Authors’ calculation
year
CREDOPROD
Total number of credit clients / number of loan
Authors’ calculation
officer
DMVOLS
if MFI offers voluntary savings to clients 1,
Authors’ calculation
otherwise 0
DEBTEQ
debt/equity ratio
Authors’ calculation
AGE
The years since an MFI started microfinance
Authors’ calculation
operations
AGE2
1
Square of age
Authors’ calculation
Authors’ calculation based on data from www.ratingfund.org
27
INFLATION
Inflation rate
World Development
Indicators (WDI)
HDI_VALUE
Human Development Index
United Nations
Development Program
(UNDP)
GDP_CONST_2000
GDP (constant 2000 US dollar )
World Development
Indicators (WDI)
28
Appendix 2: List of countries
Albania
Argentina
Chad
Chile
Honduras
India
Morocco
Mozambique
Armenia
Azerbaijan
Colombia
Croatia
Dominican
Republic
Indonesia
Jordan
Nepal
Nicaragua
Kazakhstan
Nigeria
Benin
Bolivia
Bosnia and
Herzegovina
Brazil
Bulgaria
Ecuador
Egypt
Kenya
Kyrgyzstan
Pakistan
Paraguay
El Salvador
Ethiopia
Georgia
Burkina Faso
Cambodia
Cameroon
Guatemala
Guinea
Haiti
Madagascar
Mali
Mexico
Moldova, Rep.
Of
Mongolia
Montenegro
Peru
Philippines
Romania
Russian
Federation
Senegal
South Africa
Bangladesh
Sri Lanka
Tajikistan
Tanzania, U.
Rep. Of
Timor-Leste
Togo
Trinidad and
Tobago
Tunisia
Uganda
Vietnam
29
Appendix 3: Correlation matrix explanatory variables, financial performance,
and outreach regressions
(1)
(1) FINSS
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9) (10) (11)
(12)
(13) (14) (15) (16) (17) (18)
1
(2) OPERSS
0.83
1
(3) ROA
0.65 0.69
(4) PAR30
-0.29 -0.11 -0.21
(5) LNAVLOAN
0.19 0.25 0.2 0.15
1
1
1
(6) PERCWOMAN -0.12 -0.22 -0.08 0.08 -0.65 1
(7) MF_PL
-0.03 -0.11 -0.01 0.05 -0.18 0.27
1
(8) MF_BDS
0.08 0.04 0.04 0.06 -0.09 0.03 0.58
1
(9) MF_SOCIAL
-0.11 -0.17 -0.05 0.01 -0.15 0.29 0.76 -0.1
(10) OPEREXP
0.16 0.11 0.06 0.03 0.1 -0.06 -0.06 -0.03 -0.05 1
1
(11) CREDOPROD
0.07 0.11 0.07 -0.06 -0.28 -0.02 -0.06 -0.05 -0.04 0.22
(12) DMVOLS
-0.08 -0.03 -0.04 0.15 -0.05 0.23 -0.07 -0.12 0.01 0.2 0.23
(13) DEBTEQ
0.01 0.01 0.03 -0.01 -0.02 0.11 -0.02 -0.01 -0.01 0.02 0.03 0.08
1
(14) AGE
-0.01 -0.02 0.07 0.16 0.08 0.07 0.04 -0.02 0.07 0.2 0.22 0.28
0.01
1
(15) AGE2
-0.01 -0.02 0.03 0.1 0.09 0.01 0.01 -0.03 0.04 0.14 0.18 0.24
0
0.86
(16) INFLATION
-0.09 -0.03 0.04 -0.04 -0.03 -0.01 -0.04 -0.06 0.01 -0.03 -0.05 -0.05
1
1
1
0.01 -0.01 -0.01
(17) HDI_VALUE 0.07 0.03 0.15 -0.01 0.35 -0.1 -0.01 0.07 -0.06 0 -0.26 -0.32 -0.04 0.01
0
1
0.11
1
GDP_CONS
(18) T_2000
-0.14 -0.12 -0.02 -0.06 -0.2 0.33 0.15 0 0.18 -0.04 -0.07 -0.03 0.06 0.01 0.06 0.15 0.26 1
30
Table 1: The impacts of microfinance-plus
-
Direct
-
Indirect
Expected
Solve multidimensional
problems of poverty reach
the poorest
Improve MFI customers’
human capital  MFIs’
economies of scale
Reduce default risk
Comparative advantage for
MFIs
Achieve sustainability
Improve productivity,
employment generation in
community
-
-
Unexpected
Higher operational cost
Administrative burden
Poor quality or irrelevant plus
services
Difficulty to measure good
performance
Need long time to verify the
impact of plus services
Serve as a veil to hide
inefficient performance
31
Table 2: Descriptive statistics
Mean
S.D.
FINSS
0.93
0.312
OPERSS
1.12
0.383
ROA
0.015
0.125
PAR30
0.07
0.103
LNAVLOAN
6.037
1.139
PERCWOMAN
0.729
0.251
MF_PL
0.181
0.385
MF_BDS
0.068
0.252
MF_SOCIAL
0.113
0.317
OPEREXP
9.23E+05
1.83E+06
CREDOPROD
273.775
180.784
DMVOLS
0.312
0.464
DEBTEQ
3.268
53.795
AGE
9.914
8.404
AGE2
168.887
454.217
INFLATION
0.066
0.11
HDI
0.648
0.127
GDP
1.37E+11
2.31E+11
Note: Extreme outliers (observations with age<0) are ignored
Min
0.06
0.08
-0.9
0
0
0.09
0
0
0
1144
14
0
-880.41
1
1
-0.08
0.3
2.84E+08
Max
1.94
2.95
0.79
0.98
10.11
1
1
1
1
3.80E+07
2073
1
1358.67
84
7056
1.7
0.898
8.13E+11
32
33
Table 3: Descriptive statistics for specialists and plus providers
MF_BDS=1
FINSS
OPERSS
ROA
PAR30
LNAVLOAN
PERCWOMAN
MF_SOCIAL=1
MF_FIN=1
1.026154
.822963
.9342094
(.2293195)
(.4185673)
(.3012288)
1.168077
.9222951
1.139637
(.2580093)
(.4320123)
(.3811682)
.0353333
-.0034444
.0159338
(.080327)
(.1414624)
(.1261973)
.0940984
.0715663
.0675
(.1224252)
(.1204208)
(.0991225)
5.665549
5.512657
6.129878
(1.272797)
(1.303492)
(1.082931)
.7585714
.9128571
.6949398
(.2683548)
(.1694075)
(.2492495)
Note: Standard errors are given in parentheses. Extreme outliers (observations with age<0) are ignored
34
Table 4: Financial results and microfinance plus
(2)
(3)
Operss
Finss1
-0.07877
-0.04806
(0.273)
(0.373)
Operexp
0.00000**
(0.037)
Credoprod
0.00019*
(0.094)
Dmvols
-0.04391
(0.572)
Debteq
0.00031
(0.265)
Age
0.00476
(0.624)
age2
-0.00013
(0.627)
Inflation
-0.00485
(0.963)
hdi_value
0.10761
(0.565)
gdp_const_2000
-0.00000
(0.296)
Constant
1.12865*** 0.96709*** 0.92119***
(0.000)
(0.000)
(0.000)
Observations
609
459
561
Number of id
199
146
194
within r2
e(r2_w)
0.0220
e(r2_w)
between r2
0.0269
0.0478
0.00529
overall r2
0.0112
0.0380
0.000962
VARIABLES
MF_FIN
(1)
Operss1
-0.12947**
(0.028)
(4)
(5)
(6)
(7)
(8)
Finss
Roa1
Roa
par1
par
-0.03820
-0.00795
-0.00109
0.01553
0.02596*
(0.537)
(0.624)
(0.955)
(0.276)
(0.075)
0.00000***
0.00000*
-0.00000
(0.003)
(0.096)
(0.153)
0.00025***
0.00006**
-0.00001
(0.003)
(0.048)
(0.553)
-0.14885**
-0.02364
0.03248**
(0.025)
(0.210)
(0.023)
0.00033*
0.00001
0.00005
(0.072)
(0.957)
(0.532)
0.02349***
0.00995***
0.00305
(0.002)
(0.000)
(0.117)
-0.00047**
-0.00023***
-0.00007
(0.026)
(0.002)
(0.205)
-0.11935*
-0.02977
-0.00033
(0.092)
(0.338)
(0.990)
0.15593
0.11764**
0.00733
(0.256)
(0.017)
(0.852)
-0.00000
-0.00000
-0.00000
(0.159)
(0.980)
(0.861)
0.61513*** 0.01441** -0.14011*** 0.06691*** 0.04310
(0.000)
(0.032)
(0.000)
(0.000)
(0.159)
448
905
742
884
726
148
276
225
278
224
0.147
e(r2_w)
0.0713
e(r2_w)
0.00535
0.0410
0.00154
0.0323
0.00449
0.0813
0.0583
0.000132
0.0350
0.00238
0.0527
Notes: Random effects estimates. p-values in parentheses.
*** p < 0.01. ** p < .05. * p < .1.
35
Table 5: Financial results and different types of microfinance plus
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Operss
Operss
Finss
Finss
Roa
Roa
Par
Par
0.02026
0.05692
0.08562
0.09215
0.01816
0.01243
0.02814
0.02845
(0.809)
(0.546)
(0.284)
(0.269)
(0.463)
(0.659)
(0.194)
(0.175)
mf_social
-0.24379*** -0.22210** -0.13763** -0.16441** -0.02423
-0.01150
0.00757
0.02405
(0.001)
(0.022)
(0.039)
(0.046)
(0.225)
(0.645)
(0.667)
(0.200)
operexp
0.00000**
0.00000***
0.00000*
-0.00000
(0.050)
(0.004)
(0.100)
(0.153)
credoprod
0.00019*
0.00025***
0.00006*
-0.00001
(0.099)
(0.003)
(0.050)
(0.554)
dmvols
-0.03398
-0.14060**
-0.02268
0.03265**
(0.658)
(0.032)
(0.231)
(0.023)
debteq
0.00031
0.00033*
0.00000
0.00005
(0.265)
(0.072)
(0.958)
(0.532)
age
0.00397
0.02262***
0.00988***
0.00302
(0.680)
(0.003)
(0.000)
(0.122)
age2
-0.00007
-0.00042**
-0.00023***
-0.00007
(0.773)
(0.047)
(0.003)
(0.215)
inflation
0.00159
-0.11620
-0.02930
-0.00015
(0.988)
(0.101)
(0.346)
(0.995)
hdi_value
0.08448
0.13850
0.11555**
0.00696
(0.650)
(0.311)
(0.020)
(0.860)
gdp_const_2000
-0.00000
-0.00000
0.00000
-0.00000
(0.355)
(0.197)
(0.984)
(0.869)
Constant
1.12874*** 0.98230*** 0.92123*** 0.62703*** 0.01441** -0.13883*** 0.06691*** 0.04342
(0.000)
(0.000)
(0.000)
(0.000)
(0.031)
(0.000)
(0.000)
(0.157)
Observations
609
459
561
448
905
742
884
726
Number of id
199
146
194
148
276
225
278
224
within r2
e(r2_w)
0.0223
e(r2_w)
0.149
e(r2_w)
0.0718
e(r2_w)
0.00524
between r2
0.0570
0.0801
0.0309
0.0721
0.00903
0.0340
0.00743
0.0811
overall r2
0.0302
0.0582
0.0178
0.0811
0.00387
0.0362
0.00421
0.0530
VARIABLES
mf_bds
Notes: Random effects estimates. p-values in parentheses
*** p < 0.01. ** p < 0.05. * p < 0.1.
36
Table 6: Outreach and microfinance plus
(9)
(10)
(11)
(12)
Avloan1
Avloan
Percwoman1 Percwomen
-0.64553*** -0.40032** 0.16649*** 0.11858*
(0.000)
(0.015)
(0.006)
(0.094)
Operexp
0.00000***
0.00000
(0.002)
(0.940)
Credoprod
-0.00079***
-0.00007
(0.000)
(0.758)
Dmvols
-0.18316
0.01130
(0.220)
(0.899)
Debteq
0.00005
-0.00028
(0.886)
(0.931)
Age
0.05420***
0.02221
(0.000)
(0.189)
age2
-0.00081*
-0.00077
(0.076)
(0.224)
Inflation
-0.31677***
-0.78166
(0.006)
(0.289)
hdi_value
0.55144**
-0.50530*
(0.010)
(0.094)
gdp_const_2000
-0.00000*
0.00000**
(0.061)
(0.013)
Constant
6.05792*** 5.64021*** 0.69494*** 0.91041***
(0.000)
(0.000)
(0.000)
(0.000)
Observations
919
752
104
77
Number of id
283
227
within r2
0
0.139
e(r2_w)
e(r2_w)
between r2
0.0472
0.110
e(r2_b)
e(r2_b)
overall r2
0.0331
0.111
e(r2_o)
e(r2_o)
VARIABLES
MF_FIN
Notes: Random effects estimates. p-values in parentheses
*** p < 0.01. ** p < 0.05. * p < 0.1.
37
Table 7: Outreach and different types of microfinance plus
VARIABLES
mf_bds
mf_social
(9)
LNAvloan1
-0.51481*
(0.052)
-0.72634***
(0.001)
operexp
credoprod
dmvols
debteq
age
age2
inflation
hdi_value
gdp_const_2000
Constant
Observations
Number of id
within r2
between r2
overall r2
6.05791***
(0.000)
919
283
0
0.0487
0.0338
(10)
(11)
(12)
LNAvloan Percwoman1 Percwomen
-0.27837
0.06363
0.05557
(0.246)
(0.505)
(0.574)
-0.49074** 0.21792***
0.16448*
(0.019)
(0.002)
(0.061)
0.00000***
0.00000
(0.002)
(0.988)
-0.00079***
-0.00008
(0.000)
(0.729)
-0.17323
0.01482
(0.249)
(0.868)
0.00005
-0.00094
(0.888)
(0.776)
0.05390***
0.02314
(0.000)
(0.172)
-0.00079*
-0.00081
(0.085)
(0.202)
-0.31627***
-0.83731
(0.006)
(0.259)
0.54588**
-0.46610
(0.011)
(0.126)
-0.00000*
0.00000**
(0.067)
(0.023)
5.64070*** 0.69494*** 0.89050***
(0.000)
(0.000)
(0.000)
752
104
77
227
0.139
e(r2_w)
e(r2_w)
0.112
e(r2_b)
e(r2_b)
0.113
e(r2_o)
e(r2_o)
Notes: Random effects estimates. p-values in parentheses
*** p < 0.01. ** p < 0.05. * p < 0.1.
i
ii
See www.ratingfund.org.
See www.ratingfund.org.
38