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This article examines the impact of microfinance ‘plus’ (i.e. coordinated combination of financial and nonfinancial services) on the performance of microfinance institutions (MFIs). Using a global data set of MFIs in 77 countries, we find that the provision of nonfinancial services does not harm nor improve MFIs’ financial sustainability and efficiency. The results however suggest that the provision of social services is associated with improved loan quality and greater depth of outreach.
Do Microfinance Institutions Benefit from Integrating Financial and
Nonfinancial Services?
Running head: Effects of microfinance ‘plus’
Robert Lensink
Faculty of Economics and Business, University of Groningen, Development Economics Group,
Wageningen University, The Netherlands.
E-mail:; tel: +31503633712
Roy Mersland
School of Business and Law,
University of Agder, Kristiansand, Norway
Nhung Thi Hong Vu
College of Economics, Can Tho University, Vietnam
Stephen Zamore
School of Business and Law,
University of Agder, Kristiansand, Norway
Do Microfinance Institutions Benefit from Integrating Financial and Nonfinancial
Running head: Effects of microfinance ‘plus’
This paper examines the impact of microfinance ‘plus’ (i.e., coordinated combination of
financial and nonfinancial services) on the performance of microfinance institutions (MFIs).
Using a global data set of MFIs in 77 countries, we find that the provision of nonfinancial
services does not harm nor improve MFIs’ financial sustainability and efficiency. The results
however suggest that the provision of social services is associated with improved loan quality
and greater depth of outreach.
Keywords: Microfinance ‘plus’; Business development services; Outreach; Financial
JEL codes: G21; O16; C23.
1. Introduction
Microfinance aims at providing financial services to low income households and
microenterprises who have been excluded from traditional banking. The achievement of this goal
has been universally recognized (Biosca, Lenton, and Mosley 2014, Balkenhol and Hudon 2011).
Beside this primary social mission of financial inclusion, Microfinance Institutions (MFIs) also
seek to remain financially sustainable. According to Morduch (1999), this is the “win-win”
solution of microfinance. Thus, MFIs are hybrid organization pursuing both social and financial
objectives. Like banks MFIs should be profitable or at least break-even, and like social
organizations MFIs should reach out to unbanked clients and enhance their welfare.
In the late 1970s and early 1980s, the provision of financial services to microentrepreneurs
was often done alongside nonfinancial services (social and business development services)
(Goldmark 2006). The social services focused on improving clients’ welfare while the business
development services were offered to teach the clients basic financial management principles.
This was believed to enhance clients’ business success and thereby improve MFI’s loan quality.
This belief was however not supported by early studies such as Kilby and D'Zmura (1985) and
Boomgard (1989).
While some MFIs continue to deliver nonfinancial services in recent times, many others
have phased out the practice since the late 1990s (Goldmark 2006). The focus on only financial
services (minimalist model) could among other things be attributed to low impact of the training
programs and pressure to commercialize microfinance. Often the training programs are counter-
productive because they are either of low quality or do not meet the specific needs of the poor
(Goldmark 2006, Yunus 2007).
Moreover, proponents of the minimalist approach argue that access to credit alone is enough
for the poor to work themselves out of poverty. For instance, Dr Muhammad Yunus, a renowned
pioneer of microfinance, states that “rather than waste our time teaching them new skills, we try
to make maximum use of their existing skills. Giving the poor access to credit allows them to
immediately put into practice the skills they already know” (Yunus 2007, 225). Another
argument for the minimalist approach is that, including ‘‘plus’’ services will have a negative
influence on MFIs’ financial sustainability. This argument is related to the claimed trade-off
between social mission and financial sustainability (Cull, Demirgüç-Kunt, and Morduch 2007,
2011, Hermes, Lensink, and Meesters 2011). This can be described as a “win-loss” situation for
the clients and MFIs respectively.
However, the minimalist approach has been reassessed (Lanao-Flores and Serres 2009) with
an increasing conclusion that the “microcredit, by itself, is usually not enough” (Reed 2011, 1).
To this end, some MFIs today still adopt the credit-plus model (what we call microfinance
‘plus’) by bundling financial and nonfinancial services to clients. A typical proponent of this
model is Freedom from Hunger, a U.S.- based village banking organization. Proponents argue
that, the credit-plus model maximizes MFIs’ social impact (Dunford 2001).
About 27 percent of MFIs in our sample adopt a ‘plus’ model while the remaining 73
percent follow the minimalist approach. The fact that some MFIs are specialized while others are
‘plus’ providers offers an interesting research setting. Thus, what we set out to study in this paper
is to investigate whether the microfinance ‘plus’ model is more beneficial than the minimalist
approach in terms of the achievement of MFIs’ social and financial objectives. This has not been
addressed in the academic literature to the best of our knowledge. Empirical literature on the
impact of microfinance ‘plus’ in general is very limited (Biosca, Lenton, and Mosley 2014). In
addition, we adopt several estimation methods to address potential endogeneity.
The relevance of this study is demonstrated by recent concerns that the client’s impact of
accessing stand-alone credit has been overstated (Angelucci, Karlan, and Zinman 2015, Banerjee
et al. 2015). These studies imply that providing only microcredit as a solution to poverty is
probably not adequate. According to Armendáriz and Morduch (2010), poor households benefit
from a combination of services, rather than the simple provision of credit. Similarly, Khandker
(2005) argues that because poverty is multidimensional, poor people need access to a
coordinated combination of both financial and nonfinancial services (e.g., business trainings) to
overcome poverty. Such developmental services are crucial for making credit more productive
and impactful for the clients.
The arguments for the importance of the microfinance ‘plus’ (maximalist) approach are
further supported by several studies documenting improved clients’ impact when accessing
credit in combination with nonfinancial services or ‘‘plus’’ services (Copestake, Bhalotra, and
Johnson 2001, Dunford 2001, Halder 2003, Karlan and Valdivia 2011, McKernan 2002,
Noponen and Kantor 2004, Smith 2002). A main problem with these studies, in addition to being
case studies with relatively little external validity, is that they focus on the impact of
microfinance ‘plus’ on clients, without considering the outcomes for the MFIs. In contrast, this
paper uses a global sample to investigate the potential influence of microfinance ‘plus’ on the
MFIs’ performance.
Since controversies persist between the minimalist and maximalist approaches (Bhatt and
Tang 2001, Morduch 2000), it is the aim of this paper to provide policymakers and practitioners
with informed information as to whether the provision of ‘‘plus’’ services influences the
financial and social performance of MFIs. To achieve this aim, the paper focuses on two main
questions: (1) do MFIs that combine financial and nonfinancial services achieve better financial
performance, in terms of financial sustainability, efficiency and portfolio quality, than MFIs that
deliver only financial services? and (2) do microfinance ‘plus’ providers attain better social
performance, in terms of outreach, than their specialist peers?
Using a unique sample of MFIs in 77 countries we find that there is no evidence of
microfinance ‘plus’ influence on financial sustainability and efficiency. The results however
indicate that MFIs that provide social services have higher repayment rates and greater depth of
outreach than those that do not. Thus, bundling financial services with nonfinancial further
enhance the outreach mission of MFIs (Dunford 2001).
The paper proceeds as follows. In Section 2, we discuss the concept of microfinance ‘plus’
and then provide a conceptual framework on the impact of such services on performance. This
precedes the hypothesis development. Section 3 presents the data and the specific variables used
in the estimation. Section 4 outlines the estimation procedure taking into account endogeneity
concerns. Section 5 presents and discusses the empirical results while Section 6 concludes the
paper with some remarks for practitioners and policymakers.
2. Conceptual Framework: Influence of Microfinance ‘Plus’ on MFI Performance
2.1 The Concept of Microfinance ‘plus’
Microfinance ‘plus’ services are any activities aside financial services (Goldmark 2006) targeted
at improving both the welfare of poor people and their businesses. An overall understanding of
the concept is relatively straightforward, but a more detailed explanation is also possible. For
example, an MFI that provides savings, insurance, or money transfers together with loans is not
involved in microfinance ‘plus’, because all its services are financial in nature. An MFI that
provides informational sessions to potential clients or trains existing clients in the use of credit or
the importance of repayment is not practicing microfinance ‘plus’, nor is an MFI that partners
with another organization that provides clients with ‘plus’ services. Rather, a ‘plus’ service refers
specifically to a nonfinancial service provided by the MFI itself.
Various MFIs offer a wide variety of ‘plus’ services, ranging from access to markets and
business development services (BDS) to health provision and literacy training (Goldmark 2006,
Maes and Foose 2006). In most cases, these ‘plus’ services are either BDS or social services
(Goldmark 2006). The former aims to boost competitiveness by improving productivity, product
design, service delivery or market access (Sievers and Vandenberg 2007). These services include
(but not limited to) management or vocational skills trainings, technical and marketing assistance
(Sievers and Vandenberg 2007, Goldmark 2006). Social services (e.g. health, nutrition,
education, etc.) on the other hand are intended to raise the general welfare of clients.
2.2 Conceptual Framework for the Effects of Microfinance ‘plus’
Empirical studies on the impact of microfinance ‘plus’ programs on microenterprises are limited
(Biosca, Lenton, and Mosley 2014). One of the earliest studies that evaluated the influence of
‘plus’ services in microfinance is McKernan (2002) who finds positive effect of such services on
clients’ profitability. Other impact studies include Smith ( 2002) Bjorvatn and Tungodden
(2010), Karlan and Valdivia (2011) McKenzie and Woodruff (2013), among others. The findings
of these and other studies range from no significant impact of microfinance ‘plus’ to mixed
effects. However, what seem not to be taken into account is that nonfinancial services have the
potential to influence not only the outcome for the clients but may also influence the
performance of the MFI (Sievers and Vandenberg 2007).
Thus, this study examines the influence of microfinance ‘plus’ on the institution itself and
not on the clients. Although no clear-cut theory exists on the link between microfinance ‘plus’
and performance, we can use different theories from extant literature to derive a framework that
demonstrates potential outcomes of microfinance ‘plus’ (Figure 1). Specifically, we argue that
microfinance ‘plus’ services may have both positive and negative outcomes on the performance
of MFIs. By providing ‘plus’ services, an MFI could benefit from client loyalty, potential
clients, high repayment rates, self-sustainability, better social outreach, and greater access to
client information (see top of Figure 1). On the other hand, the microfinance ‘plus’ model comes
with some challenges for the provider. Among other things, the MFI may suffer from increased
costs, resource constraints and lower client retention. (see bottom of Figure 1).
[Figure 1 near here]
Client loyalty. A key benefit of adding ‘plus’ services to microfinance is the stimulation of
client loyalty (Sievers and Vandenberg 2007). If the ‘plus’ services improve client satisfaction,
they should help increase retention rates. Such an increase in retention rate was confirmed by
Karlan and Valdivia (2011) in their randomized control trial study from Peru Another example
from Financiera Solucion, also shows that the institution benefits from including management
training because it can better retain clients (Sievers and Vandenberg 2007) which is of course
beneficial for the MFI (Reichheld 1996).
Potential clients. MFIs providing nonfinancial services have the opportunity to earn a
comparative advantage in terms of attracting new clients (Khandker 2005, Mosley and Hulme
1998) especially in the increasing competition in microfinance markets (McIntosh and Wydick
2005). Attracting more clients improves the financial sustainability of the MFI because of scale
economies (Hartarska, Shen, and Mersland 2013). And, obviously, having more clients could be
equated with greater breadth of microfinance outreach mission.
High repayment rates. Microfinance ‘plus’ can help reduce the risk of default. Relevant
training programs could for example increase the clients’ business success while trainings on
how to invest loans could help borrowers avoid using loans for consumption purpose rather than
productive activities (Marconi and Mosley 2006). For instance, Karlan and Valdivia (2011) find
some evidence of improved repayment rates arising from microfinance ‘plus’. Giné and Mansuri
(2014) however do not find evidence of improved repayment rates following clients’
participation in business training programs.
Self-sustainability. Since borrowers are often limited by their lack of knowledge they often
end up doing petty trade where even negative return on capital is a possible outcome (De Mel,
McKenzie, and Woodruff 2008). ‘Plus’ services may motivate better investments with higher
potential returns which could enhance loan repayment rates. Likewise, with improved human
capital the clients may be able to service bigger loans which enhances the financial performance
of MFIs (Hartarska, Shen, and Mersland 2013). Finally, ‘plus’ services might be offered for a
fee resulting in a positive profit margin for the MFI (Sievers and Vandenberg 2007).
Greater social outreach. By providing ‘plus’ services an MFI maximizes its social mission
with a wide range of social services such as health education (Dunford 2001). Although MFIs
aim to reach poor people, most of them access the ‘upper poor’ more than the ‘very poor’
(Mosley 2001). In addition, pressure from governments and donors to ensure financial
sustainability leads many MFIs to ignore social protection objectives and target less risky clients.
Therefore, a major argument in support of the microfinance ‘plus’ approach is that it might
enable MFIs to reach poorer and more vulnerable clients compared to the minimalist model
(Halder 2003, Maes and Foose 2006). After all, other antipoverty modalities including primary
health and education may be more effective than microfinance when wishing to enhance the
welfare of the poorest sectors (Mosley 2001). Of course, providing ‘plus’ services is not devoid
of potential disadvantages for the MFI as outlined in the following.
Increased costs. The microfinance ‘plus’ approach may come with additional operational
and administrative costs for the MFI. 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, accounted for between 4.7 and 10 percent of each MFI’s
operational costs (Vor der Bruegge, Dickey, and Dunford 1999). Also Dunford (2001)
documents that combining financial and education services offers benefits for borrowers but
increases the costs for the MFI.
Additional resources required. The provision of ‘plus’ services requires additional resources
(e.g., time, money, staff, etc.) from the institution. It increases administrative burdens and may
distract managers and other staff from credit administration, which could decrease repayment
rates (Berger 1989). Since many MFIs are already struggling with being financially self-
sustainable, adopting the maximalist model may make them worse-off. Probably, the difficulty in
being self-sustainable makes some MFIs unwilling to incorporate nonfinancial services into their
business models.
Lower client retention. Just as the provision of specific and relevant ‘plus’ services could
lead to client loyalty, poor quality or irrelevance of such services could also lead to client
dissatisfaction. Some evidence shows that microfinance borrowers do not consider training
useful and do not retain or apply their acquired knowledge, such that time spent in training
appears to be an opportunity cost for credit (Goldmark 2006). In this regards, dissatisfied clients
are more likely to stop doing business with ‘plus’ providers (Sievers and Vandenberg 2007). On
the other hand, the positive outcomes of business training on clients’ business success may also
result in reduced client retention because successful microenterprises may progress to the formal
banking sector (Karlan and Valdivia 2011).
Based on the conceptual framework above, we formulate our testable hypotheses. Given that
providers of ‘plus’ services benefit from client loyalty, possibility to attract new clients, and
income realized from demand-driven ‘plus’ services, our first hypothesis is that MFIs providing
‘plus’ services are likely to perform financially better than specialized MFIs.
Second, there is some evidence that ‘plus’ services, especially BDS, may improve the
creditworthiness of borrowers resulting in higher repayment rates (e.g., Karlan and Valdivia
2011). Therefore, we hypothesize that repayment rates in MFIs providing ‘plus’ services are
higher than in specialized MFIs. Since the positive creditworthiness effect probably holds only
for BDS providers, and not for SS ‘plus’ providers, we hypothesize that BDS ‘plus’ providers are
more effective in improving financial performance than SS ‘plus’ providers.
Third, many studies (e.g., Vor der Bruegge et al. 1999, Dunford 2001) suggest that ‘plus’
services come with additional costs for the institutions. Therefore, we hypothesize that ‘plus’
providers will experience higher costs ratios than specialists.
Finally, we hypothesize that ‘plus’ providers perform better socially than MFIs providing
only financial services. Moreover, to distinguish which ‘plus’ services lead to higher social
performance, we hypothesize that the social performance of SS providers is better than for BDS
providers. However, we must highlight that there are potential trade-offs between social and
financial performance of MFIs (Cull, Demirgüç-Kunt, and Morduch 2011) which could become
evident in our results.
3. Data and variables definitions
3.1 Data
The dataset is hand-collected from rating reports from the five leading rating agencies in the
microfinance industry; i.e. Microrate, Microfinanza, Planet Rating, Crisil and M-CRIL. The
rating reports are narratives consisting of contextual and MFI specific information including
accounting details, organizational features and benchmarks. The reports are not fully
standardized and therefore differ in their emphasis and in the amount of information available.
The result is that not all reports have information on all variables. When necessary, all numbers
in the dataset have been annualized and dollarized using the official exchange rates from the
given time. All together we used observations of 478 rated MFIs from 77 countries1 spanning the
period 1998–2012.
No dataset is perfectly representative of the microfinance field. Ours contains relatively
fewer mega-sized MFIs and does not cover all small savings and credit cooperatives. The former
are rated by agencies such 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. The data set includes specialists and providers of ‘plus’ services, so it enables
meaningful comparisons. For a further description of the dataset please see Beisland and
Mersland (2012).
3.2 Variables definitions
Dependent variables
We focus on financial sustainability, efficiency and portfolio quality as measures of financial
performance and outreach as a measure of the social performance of MFIs.
Financial sustainability measures. We consider the operational self-sufficiency ratio (OSS)
as a main indicator of financial performance. This ratio demonstrates the ability of MFIs to be
fully sustainable in the long run, in the sense that they can cover all their operating costs and
maintain the value of their capital. As a robustness check, we include financial self-sufficiency
(FSS) and return on assets (ROA) measures. Operational self-sufficiency, financial self-
sufficiency and return on assets have been used widely to measure the financial sustainability of
MFIs (Cull, Demirgüç-Kunt, and Morduch 2007, 2011, Mersland and Strøm 2009).
Efficiency measures. We use four indicators for efficiency. The operating expense ratio
1 The number of MFIs per country is avaiable from the authors upon request.
which measures the MFI’s operating expenses compared with the annual average loan portfolio.
A decrease in this ratio implies an increase in efficiency. Since MFIs offering small loans will
look worse than MFIs offering large loans we also include the cost per client variable
(Rosenberg 2009). Next, we employ the ratio of credit clients per loan officer as well as credit
clients per staff member to evaluate how ‘plus’ activities influence the employment of personnel
resources in the MFI.
Loan portfolio quality measures. We use two indicators of portfolio quality. First, the
portfolio at risk beyond 30 days (PAR30) reveals the potential for future losses based on the
current performance of the portfolio. Second, the write-off ratio measures the actual amount of
loans that have been written off as unrecoverable during a given period of time, in relation to the
outstanding loan portfolio. The variables have been used in previous studies (e.g., D'Espallier,
Guerin, and Mersland (2011)).
Social performance measures. To evaluate social performance, we use three indicators of
outreach: number of clients, average loan size and percentage of women clients. First, the
number of clients serves as a proxy for the ‘breadth of outreach’ (Rosenberg 2009, Schreiner
2002). For the ‘depth of outreach’, i.e. economic poverty level of the clients, we apply average
loan size and share of female borrowers. We recognize that average loan size and share of
female borrowers are rough proxies for ‘depth of outreach’ (for a discussion of their
shortcomings see Armendariz and Szafarz, 2011), though still the most commonly used variables
to measure clients poverty level (Hermes, Lensink, and Meesters 2011, Cull, Demirgüç-Kunt,
and Morduch 2009, 2007, Ahlin, Lin, and Maio 2011, Schreiner 2002, Mersland and Strøm
Independent variables
We distinguish three types of MFI services: (1) specialized financial services only, (2) financial
services and BDS and (3) financial services and social services (SS). We include BDS and SS
dummies, as well as a constant in our estimates. BDS equals 1 if the MFI provides business
development services and 0 otherwise. Similarly, SS equals 1 if the MFI provides social services
and 0 otherwise.
Control variables
To control for macroeconomic institutional differences we include annual percentage growth rate
of gross domestic product (GDP) (based on constant 2005 U.S. dollars) ( GDP growth) and
inflation (Claessens, Demirguc-Kunt, and Huizinga 2001, Lensink and Hermes 2004). To further
control for country influence we include the countries’ scores on the human development index
(HDI). HDI is a composite index that combines three dimensions of human development:
education, economy and life expectancy. Finally, we include regional as well time dummies in
all estimations.
To control for MFI-specific characteristics, we include number of credit officers since the
number of field officers may be driving the results and not the ‘plus’ service itself. We further
control for the size by including the total assets of the MFI. The lending methodology, either
group based or individual has the potential to influence efficiency levels, repayment as well as
outreach, thus we include group lending as a control variable regarding the repayment of credits
(Hulme and Mosley 1996, Morduch 1999). It enhances the repayment rates due to peer pressure
from other group members (Ledgerwood 1999). Furthermore, it is cost-efficient to offer group
loans due to scale economies. Group loans are less risky than are those to individuals because of
better screening, monitoring, auditing and enforcement (Ghatak and Guinnane 1999). Thus, we
expect MFIs offering group loans to have improved portfolio quality and high efficiency than
those offering individual loans. Also, in line with Mersland, Randøy, and Strøm (2011) and
Mersland, D’espallier, and Supphellen (2013), we control for MFI experience (age), whether the
MFI is a member of an international network, and whether it was initiated by a religious
organization. Finally, we control for the organizational form of the MFI (NGO, Bank,
Cooperative, and Non-Bank financial institution, and state banks). Table 1 presents a summary
of all the variables.
[Table 1 near here]
4. Estimation approach
We employ panel data modelling to examine the potential effects of microfinance ‘plus’ on the
financial and social performance of MFIs. Thus, we specify our panel model as follows:
yijt=β0+β1BDSijt +β2SSijt +γ M jt +τ MFijt +ci+εijt
where the dependent variable yijt is a measure of financial and social performance of the ith MFI
located in country jth at time t, and β0 is a constant term. BDSijt equals 1 if the ith MFI is a ‘plus’
provider that integrates BDS and 0 if it is a specialist or a ‘plus’ provider that integrates social
services in country j at time t; SSijt equals 1 if the ith MFI is a ‘plus’ provider of social services
and 0 if it is a specialist or ‘plus’ provider that integrates BDS in country j at time t. Furthermore,
Mjt is a vector of control variables describing the macroeconomic environment in country j at
time t; MFijt is a vector of control variables describing the features of the ith MFI in county jth at
time t;
is the MFI’s individual unobserved effects; and εijt is mean-zero errors.
First, we use the random effects model (RE) because our main variables of interest (i.e.,
BDS and SS) are time invariant and a fixed effects model (FE) is impossible. However, the
rejections of Hausman test null hypothesis in our results show that FE is consistent. Therefore,
our second estimator is the Hausman-Taylor’s (HT). This estimator distinguishes between
regressors that are uncorrelated with FEs and those that are potentially correlated with them.
Hausman and Taylor (1981) suggest using an economics intuition to determine which variables
should be treated as potentially correlated with the FE. The model also distinguishes time-
varying from time-invariant regressors. The model is as follows.
yijt=β0+X1ijt β1+X2ijt β2
W1ij γ1+W2ij γ2+ci+εijt
where the dependent variable yijt is a measure of performance of the ith MFI located in country j
at time t; β0 is a constant term; X denotes time-varying regressors: Inflation, GDP growth, MFI
size, MFI experience, Credit officers, HDI, and W denote time-invariant regressors; International
network, Religious organization, BDS, SS, Group lending, Coop, bank, NGO, non-bank and
are MFI-specific unobserved effects; and εijt is idiosyncratic errors. Regressors with subscripts 1
are uncorrelated with
, whereas those with subscripts 2 are specified as correlated with
. All
regressors are assumed uncorrelated with εijt .2
The MFI’s choice to integrate financial and ‘plus’ services depends substantially on its
specific characteristics. Therefore, we treat BDS and SS as endogenous. We similarly assume
that group lending is endogenous and must be instrumented. The same holds for the number of
credit officers. Group lending offers an excellent platform for the delivery of ‘plus’ services
alongside microfinance (MkNelly et al. 1996). The decision to provide individual or group
lending also depends on the presence of some MFI-specific characteristics. The remaining
2The Hausman and Taylor (1981) estimator assumes that the exogenous variables serve as their own instruments;
is instrumented by its deviation from individual means; and
is instrumented by
control variables are treated as exogenous.
The validity of instruments used in the Hausman-Taylor model is tested by Sargan-Hansen
test of overidentifying restrictions. The null hypothesis of this test is that the instruments are
valid. If the test results reject the null hypothesis (which is the case in this study), it suggests that
there are endogeneity problems other than fixed effects. This leads us to the use of Blundell and
Bond (1998) system GMM (generalised method of moments) estimator which uses lagged
differences of the dependent variable as instruments for equations in levels, in addition to lagged
levels of dependent variable for equations in the first differences (Baltagi 2013).
5. Results and discussions
5.1 Descriptive statistics and correlations
Table 2 presents descriptive statistics of all variables used in the estimations. On average, an
MFI can cover operational costs from revenue 1.13 times, indicating that the MFI is self-
sustainable. However, OSS does not depict the intrinsic self-sustainability of the MFI because of
the presence of subsidies and that is what FSS corrects for. The mean value for FSS is 0.95
which shows that on average, MFIs in our sample are not financially self-sustainable. Returns on
assets has a mean value of 2.4 percent. In terms of outreach, the average MFI has about 15000
clients of which 66 percent are women and the average loan amount disbursed (scaled by GDP
per capita) is USD 1.30. With respect to loan quality, on average, about 6 percent of the total
loan portfolio is in arrears over 30 days and 1.4 percent is written off as loan loss. Concerning
efficiency dimension, an MFI has on average, operational costs of 25 percent of gross loan
portfolio, cost per client of USD 118.65, 132 borrowers per staff, and 272 borrowers per loan
[Table 2 near here]
Furthermore, about 25 and 26 percent of MFIs offer business development and social
services respectively. The average MFI has about: USD 11.3 million of total assets, 10 years of
industry experience and 38 credit officers. Approximately 37 percent of the MFIs are members
of an international network, 17 percent of them (MFIs) were started by religious organisations
and 19 percent offer group loans only. In terms of legal status, about 51 percent of the MFIs are
NGOs, 29 percent are nonbank financial institutions, 13 percent are cooperatives and 5 percent
are banks. Finally, the mean values for GDP growth, inflation and HDI are 5.2 percent, 6.1
percent and 0.606 respectively3.
5.2 The link between microfinance ‘plus’ and MFI performance: random effects
First, we present the results of the RE estimator. Table 3 presents estimates of the effects of
microfinance ‘plus’ on financial sustainability. The statistics show that we pass the Hausman’s
test in models (1) and (2) as the p-values are greater than 0.05 but fail in model (3) because the
p-value is less than 0.05. The Wald’s chi-squared test is significant showing that our models are
correctly specified, and our regressors explain up to 27 percent of the variance of the outcome
variables (model 2) and as low as 17 percent (model 3). The results show that BDS and SS are
statistically insignificant suggesting that they have no effect on the financial sustainability of
As for the control variables we observe that HDI is negatively associated with the FSS while
MFI size significantly enhances financial sustainability. As expected, inflation reduces financial
self-sustainability of MFIs because it increases their cost of production. The results further
3 Testing (unreported) for multicollinearity problems indicates that none of the correlation values are above cut-off
point of 0.90 (Hair et al. 2010). The only correlation close to the cut-off point is that of BDS and SS (0.84)
indicating that if MFIs offer ‘plus’ services they often offer both BDS and SS.
indicate that MFIs with large number of loan officers tend to reduce financial sustainability in
terms of OSS, FSS and ROA. Similarly, MFIs with religious orientation have lower financial
sustainability compared to those without, while group lending is associated with increased ROA.
Finally we observe than any ownership type is better than being state owned when it comes to
financial sustainability. Finally, group lending is associated with increased returns on assets.
[Table 3 near here]
Table 4 also presents RE results on the link between microfinance ‘plus’ and efficiency.
Like in Table 3, BDS and SS are not significant and thus, have no effect on MFIs’ efficiency.4
[Table 4 near here]
Next, we provide the RE estimates on the link between microfinance ‘plus’ and loan quality.
Table 5 lists the results and it is clearly shown that BDS does not affect loan quality in terms of
portfolio at risk and write-offs but SS has positive outcome on the former suggesting that
providing social services enhances repayment rates. Our interpretation is that the provision of
social services enhances clients’ loyalty and therefore also their repayment of loans. Thus, clients
find the SS services relevant. The finding that MFIs do not improve repayment rates over time is
not necessarily surprising since more experienced MFIs can allow a larger share of their clients
to be in arrears.
[Table 5 near here]
Table 6 presents the last set of RE estimates on the link between microfinance ‘plus’ and
social performance. SS is significantly and positively related to women suggesting that the
provision of social services maximizes MFIs’ outreach efforts (Dunford 2001). BDS on the other
hand is insignificant and hence has no effect on social performance.
[Table 6 near here]
4 Because of space constraints we do not comment on the control variables included in tables 4, 5 and 6.
5.3 The link between microfinance ‘plus’ and MFI performance: fixed effects present
The results of the Hausman’s specification test presented in Tables 3-6 suggest that there are
fixed effects as we did not pass the test in some of the models (e.g.,3, 4, 5). To account for fixed
effects, we use the HT estimator which uses exogenous regressors as instruments. The results for
the financial sustainability are presented in Table 7 while the results for the efficiency,
repayment and outreach effects are available from authors upon request. We pass the Sargan-
Hansen test with p-values greater 0.05 in all models (Table 7) suggesting that our instruments are
valid. We however fail the test especially in three models for efficiency (unreported). Generally,
the results in the HT models mirror those of the random effects models reported in tables 3-6
the provision of ‘plus’ services does not have significant effect on the MFI’s performance.
However, the rejection of the null hypothesis of valid instruments suggests that the results may
be biased; there are real endogeneity problems aside fixed effects. Next, we employ the system
GMM to account for potential endogeneity issues.
[Table 7 near here]
5.4 The link between microfinance ‘plus’ and MFI performance: endogeneity present
Table 8 reports system GMM results on the link between microfinance ‘plus’ and financial
sustainability of MFIs. The statistics show that there is first-order serial correlation as the p-
values of AR(1) are all less than 0.05 but no second-order serial correlation (p-values>0.05). We
pass the Hansen’s test of overidentifying restrictions indicating joint validity of instruments set
(all p-values > 0.05). All the lags of the dependent variables are statistically significant at least at
the 5 percent level. Once again, neither BDS nor SS are significantly associated with the
financial sustainability confirming the results previously reported. Likewise, we find that the
GMM regressions do not result in significant findings for the effect of BDS or SS on the
efficiency, repayment or social outreach of the MFI (unreported).
[Table 8 near here]
A concern with the system GMM estimates relates primarily to our time-invariant regressors
(i.e., BDS and SS) as their lagged values cannot be used as instruments because their lagged first
differences are zero. This leaves us with first differences of time-varying variables which
unfortunately cannot be valid instruments either because they suffer from Nickell’s bias (Nickell
1981) and do not also correlate sufficiently with the observed BDS and SS. Thus, the estimates
of the system GMM are also problematic. Therefore, the random effects estimates are preferred
because of the nature of our variables of interests which get wiped out if the fixed effects model
is used and their estimation in the HT model is not appropriate due to invalidity of instruments.
In any case, results from the three estimators (RE, HT and system GMM) suggest that
microfinance ‘plus’ do not influence overall performance of MFIs. Only in few cases the RE
estimates provide some evidence of improved loan quality and outreach and thus support our
hypotheses on these dimensions of performance.
6. Conclusion
This paper set out to examine the potential impact of microfinance ‘plus’ on the financial and
social performance of microfinance institutions (MFIs). Impact studies of nonfinancial services
have always used the clients as their unit of analysis. In contrast, this paper focuses on the
providers of ‘plus’ services. Using a unique global sample of MFIs and an arsenal of estimation
methods, we find insignificant impact of business development services on MFIs’ financial and
social performance. Furthermore, we find only meagre evidence of improved loan quality and
outreach with the provision of social services. Specifically, providing social services comes
with lower portfolio at risk and more women clients though these findings are not stable across
estimation methods.
Thus, this paper provides a first-hand information on the outcome of microfinance ‘plus’
from the perspective of the providers. Overall, it appears there is no performance disparity for
those MFIs providing ‘plus’ services and those that do not. Perhaps, the benefits of microfinance
‘plus’ might have been neutralised by the disadvantages associated with it, hence, leaving a
negligible net impact on MIFs’ performance.
The no-results reported in this study actually offers important policy lessons for MFIs. With
this information, microfinance practitioners are informed that, adopting the maximalist approach
causes no harm on their overall financial and social performance. Thus, if the ‘plus’ services are
of value for the customers the provision of such does not harm the performance of the MFI. We
do however recognize that the design and the cost structure of the ‘plus’ service does of course
influence the outcome for the client as well as the MFI. Our study only shows that MFIs offering
‘plus’ services today have on average been able to design these in such a way that they do not
harm the performance of the MFIs. We thus recommend future studies to look deeper into how
the design and cost structure of ‘plus’ services have an influence on the MFI performance.
Likewise, an interesting area for future researchers could be an investigation of how “smart
subsidies” (Morduch 2007) might account for the additional costs of providing ‘plus’ services, as
well as how coordinated nonfinancial services provided by non-MFIs, in cooperation with MFIs,
might influence MFI performance. Finally, like Berge, Bjorvatn, and Tungodden (2014), studies
are much warranted on whether or not different ‘plus’ services actually enhance clients’
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Microfinance ‘plus’ outcomes
Figure 1: Effects of microfinance ‘plus’ on microfinance institutions’ performance
Increased costs
Additional resources required
Lower client retention
Customer loyalty
Potential customers
High repayment rates
Financial Self-sustainability
Greater social outreach
Access to client information
MFI nancial and
social performance
Variables Description
Operational self-sufficiency Operating revenue / (Financial expense + loan loss
provision expense + operating expense)
Financial self-sufficiency Adjusted operating revenue / adjusted (financial expense +
loan loss provision expense + operating expense)
Return on Assets Net operating income / average total assets
Portfolio at risk (PAR30) Portfolio at Risk > 30 days/ Gross portfolio
Write-off ratio Write-off of loans / Average gross portfolio
Clients Number of active clients
Average loan size Amount issued in the period / Number of issued loans
Women Percentage of female clients
Operating expense ratio Operating expenses/average gross loan portfolio
Cost per client ratio Operating expenses/ number of active clients
Staff productivity Number of active borrowers/ Number of staff
Loan officer productivity Number of active borrowers / Number of loan officers
BDS 1 if MFI provides business development services, 0
SS 1 if MFI provides social services, 0 otherwise
Group lending 1 if MFI uses group lending methodology, 0 otherwise
MFI experience (age) Number of years the MFI has been in operation
Credit officers Number of credit officers an MFI has at the end of year
Assets Total assets of the MFI
Bank 1 if a MFI is registered as a bank, 0 otherwise
Nonbank 1 if a MFI is registered as a non-financial institution, 0
NGO 1 if a MFI is registered as non-governmental organization,
0 otherwise
Coop 1 if a MFI is registered as a cooperative, 0 otherwise
International network 1 if the MFI is member of an international network, 0
Religious organization 1 if the MFI was initiated by an organization with a
religious agenda, 0 otherwise
GDP growth Annual GDP growth (based on constant 2005 US dollars)
HDI Human Development Index
Inflation Annual inflation rate
Table 1: Variable descriptions
Table 2: Descriptive statistics
Variable Mean Std. Dev. Min Max
Operational self-sufficiency 1.128241 0.3678306 0.075 2.96
Financial self-sufficiency 0.9484163 0.3047077 0.063 3.469
Return on assets 0.0240719 0.0858322 -0.373 0.373
Number of clients 15008.51 18951.42 24 98639
Average loan size 1.296353 2.826229 0.027 35.72
Percentage of women 0.6646034 0.2601223 0.000 1.000
Portfolio at risk 0.0601583 0.0689986 0.001 0.39
Write-off ratio 0.0135395 0.0196164 0.000 0.099
Write-off ratio (log) -5.053952 1.616904 -6.907 0.948
Operating expense ratio 0.2458689 0.1269165 0.016 0.6
Cost per client 118.648 107.004 0.242 574.99
Borrowers per staff member 132.1854 111.304 1 1893
Borrowers per loan officer 272.4617 159.7607 3 989
Assets 11301397.26 24831411.
19288 279350816
MFI age 9.782793 5.828356 0 29
Group lending 0.1923767 0.3942558 0 1
Credit officers 38.10859 39.05367 1 199
International network 0.3729858 0.483713 0 1
Religious organization 0.1685289 0.3744224 0 1
BDS 0.2524664 0.4345248 0 1
SS 0.2699552 0.4440358 0 1
Bank 0.0483496 0.2145538 0 1
Nonbank 0.2924221 0.454981 0 1
NGO 0.5099954 0.5000163 0 1
Coop 0.1338912 0.3406146 0 1
GDP growth 5.206064 3.175086 -14.149 17.33
Inflation 0.0611677 0.0487948 -0.185 0.287
HDI 0.6060426 0.1358599 0.058 0.806
Table 3: The link between microfinance ‘plus’ and financial sustainability
(1) (2) (3)
Variables OSS FSS ROA
BDS 0.0089 -0.0214 -0.0067
(0.0333) (0.0270) (0.0095)
SS -0.0060 0.0030 0.0072
(0.0292) (0.0249) (0.0097)
HDI -0.2367 -0.2811** -0.0170
(0.1769) (0.1408) (0.0642)
GDP growth 0.0023 0.0057* 0.0013
(0.0046) (0.0035) (0.0010)
MFI size 0.1342*** 0.1075*** 0.0248***
(0.0207) (0.0159) (0.0038)
MFI experience -0.0069 -0.0072 0.0005
(0.0047) (0.0044) (0.0007)
Inflation -0.1548 -0.7004*** 0.0737
(0.2662) (0.2398) (0.0677)
Credit officers -0.0026*** -0.0017*** -0.0004***
(0.0007) (0.0005) (0.0001)
International network -0.0399 0.0109 0.0003
(0.0471) (0.0358) (0.0086)
Religious organization -0.0463 -0.0837* -0.0193*
(0.0534) (0.0430) (0.0100)
NGO 0.3541 0.3995*** 0.0346
(0.3560) (0.1318) (0.0457)
Non-bank 0.2093 0.3175** 0.0170
(0.3557) (0.1261) (0.0459)
Bank 0.3720 0.3933*** 0.0385
(0.3645) (0.1462) (0.0473)
Coop 0.3281 0.4057*** 0.0306
(0.3565) (0.1368) (0.0466)
Group lending 0.0447 0.0333 0.0187***
(0.0329) (0.0264) (0.0065)
Constant -0.8750* -0.7562*** -0.3634***
(0.4797) (0.2712) (0.0853)
Time dummies Yes Yes Yes
Regional dummies Yes Yes Yes
Observations 628 654 1,104
Number of MFIs 196 211 317
Hausman test (p-value) 0.7758 0.4205 0.0016
R-squared (overall) 0.2071 0.2658 0.1688
Chi-squared 142.12*** 306.36*** 133.38***
Notes: This table lists Random effects results of the link between microfinance ‘plus’ and financial sustainability
of MFIs. OSS is operational self-sustainability and measures the ability of MFI to cover its operational costs from
revenue, FSS is financial self-sustainability and measures the ability of MFI to cover operational costs from revenue
without subsidies and ROA is returns on assets. BDS=1 if MFI provides business development services,
0=otherwise, and SS=1 if MFI provides social services, 0=otherwise. MFI size is the natural logarithm of total
assets, MFI experience is the number of years the MFI has been in operation, and Credit officers is the number of
credit officers at the end of the year. Group lending=1 if MFI offers group loans, 0= otherwise, International
network=1 if MFI is a member of international network, 0=otherwise, Religious organisation=1 if MFI was started
by a religious organisation, 0=otherwise. NGO =1 if the MFI is registered as a nongovernmental organisation, 0
=otherwise, Non-bank =1 if the MFI is registered as a non-bank financial institution, 0 =otherwise, Bank =1 if the
MFI is registered as a bank, 0 =otherwise, and Coop =1 if the MFI is registered as a cooperative, 0 =otherwise. GDP
growth is the real annual Gross Domestic Product growth rate, Inflation is annual producer price index, and HDI is
human development index. In parentheses are robust standard errors.
*, **, and *** denote statistical significance at the 10%, 5%, 1% respectively.
Table 4: The link between microfinance ‘plus’ and MFI efficiency
(4) (5) (6) (7)
Variables Operating
Cost per
Credit officer
BDS 0.0046 -11.1686 -6.4027 -13.6241
(0.0092) (8.2730) (4.6786) (9.7459)
SS -0.0006 7.3049 1.8171 1.3546
(0.0102) (7.2725) (4.6595) (10.1066)
HDI -0.1051 100.1630 84.3848* 61.4425
(0.0999) (76.6951) (44.5177) (117.7688)
GDP growth 0.0010 -1.8255** 0.6072 0.8140
(0.0011) (0.7907) (0.6034) (1.3391)
MFI size -0.0551*** 12.6214* 16.3686*** 39.5467***
(0.0066) (6.7782) (3.6843) (7.1674)
MFI experience -0.0009 0.2095 0.7911 1.9210
(0.0015) (1.2514) (0.8511) (1.7786)
Inflation -0.0367 -6.5753 -82.5389** -165.1948*
(0.0876) (62.6171) (41.7542) (86.9073)
Credit officers 0.0006*** -0.3000** -0.2736** -1.2017***
(0.0002) (0.1443) (0.1184) (0.2305)
International network 0.0463*** -8.9624 21.2268** 58.0469***
(0.0147) (10.9173) (9.9890) (19.0053)
Religious organization -0.0235 -6.6840 26.6914* 17.3264
(0.0167) (13.1452) (15.0120) (23.1394)
NGO -0.0829** 4.1400 -31.1030 -28.3443
(0.0382) (37.1670) (18.9918) (37.8816)
Non-bank -0.0907** 31.7750 -40.0253** -39.4110
(0.0373) (36.5450) (18.8842) (35.8501)
Bank -0.0599 -16.4869 -76.2367** -19.1276
(0.0449) (47.5149) (30.9760) (57.5899)
Coop -0.1948*** -29.9296 -76.8696*** -69.6188
(0.0416) (39.1691) (22.6003) (42.7219)
Group lending -0.0137** -2.0071 0.4042 8.5278
(0.0067) (6.0482) (3.9206) (8.6970)
Constant 1.2140*** -152.1842 -135.6015** -334.4640**
(0.1207) (111.7720) (63.4283) (132.5162)
Time dummies Yes Yes Yes Yes
Regional dummies Yes Yes Yes Yes
Observations 994 960 1,123 1,106
Number of MFIs 295 278 315 313
Hausman test (p-value) 0.0001 0.0002 0.9036 1.0000
R-squared (overall) 0.3410 0.2724 0.1924 0.2093
Chi-squared 334.69*** 266.08*** 172.43*** 154.27***
Notes: This table lists Random effects estimates of the link between microfinance ‘plus’ and MFI efficiency.
Operating expense is total operating expenses as a percentage of average gross loan portfolio, Cost per client is total
operating expenses as a percentage of number of active clients, Staff productivity is the number of active borrowers
per staff, and Credit officer productivity is the number of active borrowers per credit officer. Regressors are defined
previously. In parentheses are the robust standard errors.
*, **, and *** denote statistical significance at the 10%, 5%, 1% respectively.
Table 5: The link between microfinance ‘plus’ and loan quality
(8) (9)
Variables PAR30 Write-off
BDS 0.0038 0.1091
(0.0054) (0.2420)
SS -0.0110** -0.3611
(0.0055) (0.2361)
HDI 0.0330 -0.8982
(0.0504) (0.9150)
GDP growth -0.0023*** -0.0244
(0.0006) (0.0206)
MFI size -0.0055 0.0935
(0.0033) (0.0701)
MFI experience 0.0023*** 0.0169
(0.0007) (0.0159)
Inflation -0.0628 1.4634
(0.0431) (1.1286)
Credit officers 0.0001 -0.0008
(0.0001) (0.0021)
International network -0.0234*** -0.1109
(0.0073) (0.1565)
Religious organization 0.0082 0.1442
(0.0083) (0.1959)
NGO 0.0177 0.5172
(0.0332) (0.5032)
Non-bank 0.0221 0.2957
(0.0333) (0.5000)
Bank 0.0054 0.0621
(0.0357) (0.5943)
Coop 0.0327 -0.0124
(0.0347) (0.5327)
Group lending 0.0023 0.2515*
(0.0044) (0.1404)
Constant 0.0939 -7.0021***
(0.0698) (1.2779)
Time dummies Yes Yes
Regional dummies Yes Yes
Observations 1,001 1,087
Number of MFIs 298 301
Hausman test (p-value) chi2<0 0.4105
R-squared (overall) 0.1640 0.0913
Chi-squared 117.50*** 228.54***
Notes: This table lists Random effects estimates of the link between microfinance ‘plus’ and loan portfolio quality
of MFIs. PaR30 is nonperforming loans over 30 days, and Write-off is natural logarithm of the proportion of loans
portfolio that have been written off as loan loss. Regressors are defined previously. In parentheses are robust
standard errors.
*, **, and *** denote statistical significance at the 10%, 5%, 1% respectively.
Table 6: The link between microfinance ‘plus’ and social performance
(10) (11) (12)
Variables Clients Average loan size Women
BDS -602.9183 -0.0212 -0.0098
(777.4759) (0.1556) (0.0443)
SS 597.1599 0.0755 0.0899**
(699.2822) (0.1505) (0.0431)
HDI 3,861.4355 -1.6081 0.4286**
(5,486.8614) (1.4455) (0.2067)
GDP growth 110.2542 -0.0238 0.0143**
(83.0698) (0.0348) (0.0065)
MFI size 1,933.2793*** 0.1736* -0.0615***
(516.9265) (0.1006) (0.0202)
MFI experience 142.4659 -0.0321 0.0038
(115.0366) (0.0349) (0.0043)
Inflation -5,247.5854 -2.1151 -0.5878*
(6,821.1764) (2.8034) (0.3159)
Credit officers 222.4752*** -0.0022 0.0009**
(21.2049) (0.0038) (0.0004)
International network 2,452.8597* -0.3416 0.1434***
(1,290.6792) (0.4111) (0.0401)
Religious organization -1,606.7106 0.3312 -0.0466
(1,166.1896) (0.5857) (0.0602)
NGO -2,557.9972 0.7308** -0.0822
(2,521.8525) (0.3527) (0.0728)
Non-bank -1,930.1692 1.6658** -0.1872**
(2,504.2784) (0.6494) (0.0806)
Bank -2,524.7437 2.3336** -0.2099**
(3,992.8307) (1.0651) (0.1055)
Coop 3,843.7740 1.3902** -0.2162*
(3,551.6547) (0.5984) (0.1105)
Group lending 82.3783 -0.0524 0.0214
(525.3579) (0.2298) (0.0268)
Constant -32,712.4700*** -1.0653 1.2537***
(8,845.9372) (1.9017) (0.3633)
Time dummies Yes Yes Yes
Regional dummies Yes Yes Yes
Observations 976 645 176
Number of MFIs 277 201 139
Hausman test (p-value) 0.2034 0.0000 0.3599
R-squared (overall) 0.6376 0.1521 0.4716
Chi-squared 827.32*** 66.19*** 229.78***
Notes: This table lists Random effects estimates of the link between microfinance ‘plus’ and social performance of
MFIs. Clients is the number of active clients an MFI has, Average loan size is the amount of loan disbursed per
borrower scaled by gross domestic product per capita, and women is a percentage of female clients. Regressors are
defined previously. In parentheses are robust standard errors.
*, **, and *** denote statistical significance at the 10%, 5%, 1% respectively.
Table 7 The link between microfinance ‘plus’ and financial sustainability
(13) (14) (15)
Variables OSS FSS ROA
BDS -0.0114 -0.0302 -0.0099
(0.0514) (0.0339) (0.0106)
SS -0.0023 0.0017 0.0066
(0.0492) (0.0326) (0.0104)
HDI -0.0794 -0.0837 0.0598
(0.2881) (0.2324) (0.0592)
GDP growth 0.0030 0.0064* 0.0014
(0.0050) (0.0034) (0.0010)
MFI size 0.1507*** 0.1551*** 0.0350***
(0.0260) (0.0191) (0.0048)
MFI experience -0.0090 -0.0067 0.0003
(0.0056) (0.0056) (0.0009)
Inflation -0.1246 -0.6438*** 0.0731
(0.3045) (0.2235) (0.0591)
International network -0.0485 -0.0112 0.0007
(0.0563) (0.0573) (0.0104)
NGO 0.5578** 0.5296*** 0.0591*
(0.2845) (0.1549) (0.0355)
Non-bank 0.4077 0.4339*** 0.0363
(0.2826) (0.1422) (0.0348)
Credit officers -0.0025*** -0.0024*** -0.0007***
(0.0009) (0.0006) (0.0002)
Group lending 0.0611 0.0429* 0.0252***
(0.0386) (0.0242) (0.0074)
Religious organization -0.0386 -0.0808 -0.0208
(0.0630) (0.0653) (0.0129)
Bank 0.5090* 0.4489** 0.0549
(0.2963) (0.1986) (0.0402)
Coop 0.5225* 0.5182*** 0.0460
(0.2833) (0.1609) (0.0370)
Constant -1.4732** -1.7077*** -0.5844***
(0.6083) (0.3850) (0.1012)
Time dummies Yes Yes Yes
Regional dummies Yes Yes Yes
Observations 628 654 1,104
Number of MFIs 196 211 317
Chi-squared 106.24*** 262.62*** 199.78***
Sagran-Hansen (P-value) 0.6688 0.1783 0.2927
Notes: This table presents estimates of the Hausman-Taylor model. Our endogenous regressors are credit officers,
BDS, SS, and Group lending, of which credit officers is time varying and the rest are time-invariant. The remaining
regressors are considered exogenous. Time varying exogenous variables are HDI, GDP growth, MFI size, MFI
experience and inflation. The remaining exogenous regressors are time invariant. Variables are defined in Table 2.
Standard errors in parentheses.*** p<0.01, ** p<0.05, * p<0.10.
Table 8: The link between microfinance ‘plus’ and financial sustainability
(16) (17) (18)
Variables OSS FSS ROA
OSSt-1 0.4490**
FSSt-1 0.4881**
ROAt-1 0.5066***
BDS 0.1630 0.0109 0.0009
(0.1221) (0.1047) (0.0132)
SS -0.0864 0.0743 0.0011
(0.1477) (0.1745) (0.0131)
HDI -0.2846 0.3117 0.0236
(0.2883) (0.6601) (0.0646)
GDP growth -0.0007 0.0128 0.0012
(0.0060) (0.0124) (0.0008)
MFI size 0.0468* 0.0703 0.0025
(0.0266) (0.0725) (0.0031)
MFI experience 0.0019 -0.0201 -0.0009*
(0.0067) (0.0205) (0.0005)
Inflation 0.1433 -0.1500 0.0550
(0.5422) (0.6218) (0.0749)
Credit officers -0.0010 -0.0007 -0.0000
(0.0008) (0.0013) (0.0001)
International network 0.0518 -0.0541 0.0036
(0.0593) (0.1124) (0.0045)
Religious organization 0.0003 -0.0590 0.0085
(0.0464) (0.0993) (0.0075)
NGO -4.5378 4.1261 -0.1938
(5.3656) (6.0511) (0.3040)
Non-bank -4.7924 4.3736 -0.2106
(5.4818) (6.3937) (0.3170)
Bank -4.4579 4.0063 -0.1954
(5.3021) (5.9865) (0.3022)
Coop -4.5834 4.0857 -0.2145
(5.3237) (6.0198) (0.3056)
Group lending -0.0672 -0.0698 -0.0046
(0.0678) (0.0642) (0.0120)
Constant 4.7866 -4.7093 0.1909
(5.4758) (7.0737) (0.3576)
Time dummies Yes Yes Yes
Regional dummies Yes Yes Yes
Observations 466 472 844
Number of MFIs 187 201 305
Number of instruments 41 41 43
Chi-squared 229.83*** 210.41*** 321.87***
AR(1) test (P-value) 0.045 0.033 0.000
AR(2) test (P-value) 0.412 0.296 0.792
Hansen test (P-value) 0.800 0.284 0.176
Notes: This table lists system GMM (generalized methods of moments) results of the link between microfinance
‘plus’ and financial sustainability of MFIs. OSS is operational self-sustainability and measures the ability of MFI to
cover its operational costs from revenue, FSS is financial self-sustainability and measures the ability of MFI to cover
operational costs from revenue without subsidies and ROA is returns on assets. Regressors are defined previously.
AR (1) and AR (2) are tests for first-and second-order serial correlation in the first-differenced residuals, under the
null hypothesis of no serial correlation. The Hansen test of over-identification is under the null hypothesis that all
instruments are valid. In specifying the two-step System GMM model, we use lags of: dependent variables, BDS
and SS as GMM instruments allowing the default lags limits in Stata. “By default, gmmstyle() generates the
instruments appropriate for predetermined variables: lags 1 and earlier of the instrumenting variable for the
transformed equation and, for system GMM, lag 0 of the instrumenting variable in differences for the levels
equation” (Roodman 2009, 124). The exogenous regressors are also standard instrumental variables, and the
‘collapse’ option is used to limit instrument proliferation. In parentheses are robust standard errors.
*, **, and *** denote statistical significance at the 10%, 5%, 1% respectively.
... The panel regression model was employed to examine the stated (Bell et al., 2019). The Hausman test results suggested the fixed effects (FE) estimator to be appropriate over the random effects (RE) estimator (Lensink et al., 2017). ...
... The link test for model specification results shows the p-value of 0.766 greater than 0.05 suggesting that the model is correctly specified (Lensink et al., 2017). The explanatory variables explain about 38% (R-squared-within) of the variation in the outcome variables. ...
Purpose Following the COVID-19 outbreak, various economies imposed different financial interventions as part of initiatives to cushion their stock markets from deteriorating performance. Our article examines the effectiveness of these interventions in protecting stock markets during the pandemic. Design/methodology/approach The authors employ Panel Vector Autoregression to model the magnitude and timing of shocks from COVID-19 to stock markets. The fixed effects regression is then utilized to assess the role of financial interventions in protecting stock markets during COVID-19. The study uses daily stock index returns as well COVID-19 containment measures stringency index data from 39 countries ranging from 2nd January 2020 to 30th September 2021. Findings Our findings firstly reveal a significant positive stock market reaction to country-level containment measures stringency but only during the first wave of COVID-19. We secondly show that stock market functioning interventions that include short selling bans and circuit breakers amplify the positive effects of COVID-19 containment measures stringency on stock market performance. Research limitations/implications The authors stress the need for policymakers and regulators to timely intervene in protecting economies and stock markets during crises such as COVID-19 in order to reduce panic among investors. Moreover, investors should adjust their portfolios by investing in stocks from countries that have proper financial market interventions in place. Originality/value Despite growing body of literature on COVID-19 and stock market performance, there is limited evidence on the role of financial sector interventions to cushion stock markets during tumultuous conditions caused by the pandemic.
... However, there are other reasons why it may be more expensive to serve women in male-dominated societies. First, women may require additional costly services that are tailored to their specific needs, such as nutrition, health, education, business development services, and gender-awareness training of staff (Goldmark, 2006;Lensink, Mersland, Vu & Zamore, 2018). These may be needed to help the businesses of women and to boost their self-worth, because many women in discriminatory environments lack basic skills, training, and education (Kabeer, 2005;Lensink et al., 2018). ...
... First, women may require additional costly services that are tailored to their specific needs, such as nutrition, health, education, business development services, and gender-awareness training of staff (Goldmark, 2006;Lensink, Mersland, Vu & Zamore, 2018). These may be needed to help the businesses of women and to boost their self-worth, because many women in discriminatory environments lack basic skills, training, and education (Kabeer, 2005;Lensink et al., 2018). Second, crossing cultural barriers to reach marginalized women can result in further costs due to relationship problems (e.g., mistrust between male loan officers and female clients) as well as coordination and communication challenges (Wry & Zhao, 2018). ...
Much of the microfinance rhetoric revolves around fighting female poverty, which is often the result of discriminatory gender norms. Also, the microfinance industry has always been influenced by foreign actors, who, according to the literature, promote women’s empowerment. Yet, little is known about how microfinance institutions’ (MFIs) outreach to women is affected by the interplay between societal norms and the actions of these foreign actors. In response, this study draws on two streams of institutional theory, institutional logics perspective and institutional work theory, to investigate the influence of gender discrimination on microfinance outreach to women and to test the moderating effect of an international founder. Using data on 213 MFIs from 65 countries, the results show that gender discrimination negatively impacts microfinance outreach to women, but that the negative effect is mitigated by having an international founder. These findings are discussed, and several avenues are opened for future research.
... Khandker (2005) suggests that because of the multidimensional nature of poverty, poor people need access to a coordinated combination of both financial and nonfinancial assistance to overcome poverty. Such developmental services are vital for making credit more fruitful and impactful for clients (Lensink et al., 2018). To address the various financial and non-financial needs of clients, MFIs are incorporating new nonfinancial services/products that not only attract more clients but also sustain their growth and financial sustainability (Grant 2000 ...
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... Finally, the area experts applied the manual filtration process to exclude the irrelevant and incomplete research articles to arrive at a final number of 61 articles for analysis. Our methodology is in line with the standard approaches of meta-analysis in socio-economic fields (Lensink et al., 2018). We conducted an in-depth analysis by carefully examining the published articles to capture the most relevant information. ...
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... It measures an institution's ability to generate sufficient revenue to cover its costs. It is a MIX Market standard indicator for financial performance and has been used as a proxy for financial sustainability in various studies [8,33,48,53,[61][62][63][64]. Achieving above 100% of OSS indicates that the institution is earning sufficient revenue from its operations to cover its costs. ...
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Increasing institutional capital through deposit mobilization keeps the cost of capital low, thus leading to financial sustainability. However, little is known about how deposit mobilization affects financial sustainability. Using balanced panel data of 166 rural savings and credit cooperatives (RUSACCOs) from Ethiopia over the period of 2014–2016, we investigated the effect of deposit mobilization on financial sustainability. The results of the panel regression estimates showed that, among the deposits mobilization variables, the deposit to loan ratio, deposit to total asset ratio, the volume of deposits, and demand deposit ratio had a significant direct impact on financial sustainability. The fixed effect regression result for interest rate spread showed that an inverse relationship existed between the interest rate spread and financial sustainability. Furthermore, according to our robust fixed effect regression results, among the control variables, the age of the institution and inflation rate affects financial sustainability. Contrary to our expectations, the number of members and the percentage of woman members were not significant. This may be attributed to the fact that some members were inactive for a long period. We suggest that RUSACCOs should focus on deposit mobilization specifically on demand deposits and keep the interest rate spread narrower to ensure their sustainability.
... As a result, most microfinance institutions (MFIs) are now concentrating on loan management and deposit growth. It has been investigated that those social services are associated with higher loan quality and a greater depth of outreach(Lensink et al., 2018)(Shivaprasad & Anilkumar, 2019). ...
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Microfinance’s Role in Poverty Alleviation in Pakistan
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In Vietnam after 30 years of the renewal process, due to a number of factors, especially the industrial sector, the growth of labor productivity has been increased significantly. By using shift-share analysis method to intra-industry between 1996 and 2017, which focused on internal industry, the result showed that both intra-effect and static shift effect made a great contribution to the labor productivity growth of the economy, and the contribution of static effect tends to increase. This means that the movement of labor from inefficient sectors to the more efficient sectors has had a positive impact on the overall productivity growth rate. Therefore, in order to promote productivity growth in the economy, Vietnam has to implement solutions in terms of reallocating resources, transforming the economic structure, applications of technology, and training human resources.
... There was relationship between inflation rate and financial sustainability, and inflation negatively affected financial sustainability (Duguma & Han, 2018). At the same time, macroeconomic variable such as inflation was also found to have an effect on the microfinance institution self-sufficiency and inflation increased the cost of production, which had led to decrease in financial sustainability of the microfinance institutions (Lensinka et al., 2018). ...
Since the passage of Law on Tourism in 2005 and its amendment in 2017, Vietnam has increasingly invested in the tourism sector as a spearhead industry of the economy to turn Vietnam into a destination for the mass tourist. Along with tourism development, the negative impacts of tourism activities on the environment and society have been acknowledged to a certain extent. In such a context, sustainable tourism, ethical tourism, and responsible tourism have been discussed. However, there is a lack of previous research on how the responsible tourism concept is adapted to the situation of the Vietnam tourism industry. This paper presents the background of the Vietnamese tourism industry. Moreover, based on the survey results with 122 individuals and 20 tourism experts, this paper highlights prominent actions to promote responsible tourism in Vietnam. The implications for further research on the topic are also proposed.
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This paper addresses the way microfinance programs affect food security, which is compiled based on a literature review of a total of 58 pieces of literature, from 1995 to 2020. Its paper sheds light on microfinance in rural areas in Indonesia, microfinance and food security, and critics related to microfinance programs based on a literature review. The result indicates that when the program's impact on participants' families' food security and nutrition is measured, the results could be different. Most of the results stated a positive impact, but it might depend on many other factors. Taken together, the paper findings highlight the importance of a cycle of innovation, experimentation, and evaluation that must be carried out to build a robust financial institution to answer challenges and provide solutions to all the various conditions experienced by low-income families, involving various institutional structures, modes, and mechanisms. Other supporting factors are also important, including community involvement, the availability of safety nets, and non-financial support in many fields, such as increasing institutional capacity, business development, technology utilization, procurement, production, and, most importantly, marketing. Abstrak Jurnal ini membahas cara program keuangan mikro mempengaruhi ketahanan pangan. Jurnalnya menyoroti keuangan mikro di daerah pedesaan di Indonesia, keuangan mikro dan ketahanan pangan, dan kritik terkait program keuangan mikro berdasarkan tinjauan literatur. Hasilnya menunjukkan bahwa ketika dampak program terhadap ketahanan pangan dan gizi keluarga peserta diukur, hasilnya bisa berbeda. Sebagian besar hasil menyatakan dampak positif, tetapi mungkin tergantung pada banyak faktor lain. Secara keseluruhan, temuan makalah menyoroti pentingnya siklus inovasi, eksperimen, dan evaluasi yang harus dilakukan untuk membangun lembaga keuangan yang tangguh untuk menjawab tantangan dan memberikan solusi atas berbagai kondisi yang dialami oleh keluarga berpenghasilan rendah, yang melibatkan berbagai struktur, mode, dan mekanisme kelembagaan. Faktor pendukung lainnya juga penting, antara lain keterlibatan masyarakat, ketersediaan jaring pengaman, dan dukungan non-finansial di berbagai bidang, seperti peningkatan kapasitas kelembagaan, pengembangan usaha, pemanfaatan teknologi, pengadaan, produksi, dan yang terpenting pemasaran. Kata kunci: keuangan; akuntansi; program keuangan mikro; ketahanan pangan; Indonesia
While entrepreneurship training is essential for the growth and sustainability of enterprises, the literature identifies several challenges which contribute to insufficient transfer (application) of trained materials to enterprises. Yet, the extant literature on training transfer is inconclusive, with minimal focus on trainee personal characteristics and scarce visualization of transfer as a dimensional concept. The study addressed this gap by examining the influence of selected trainee demographics on dimensions of near, far and creative transfer of entrepreneurship training. Based on a survey of 418 trainees in Tanzanian community‐based microfinance institutions, findings reveal that, each dimension of training transfer tested was influenced by a different set of demographic determinants. It was evident that elders were less enthusiastic about near and far application of entrepreneurship training. Males perceived slightly more training transfer in far and creative domains while those with higher education levels were more likely to apply training in all transfer dimensions. Those with exposure to entrepreneurship were more convinced of the value of applying the trained skills to near and creative domains. Consequently, the study advances Andragogy by showing the contextual nature of applicability of its principles, as well as the dependence of training transfer on contextual factors surrounding trainees.
This paper sheds light on a poorly understood phenomenon in microfinance which is often referred to as “mission drift”: A tendency reviewed by numerous microfinance institutions to extend larger average loan sizes in the process of scaling–up. We argue that this phenomenon is not driven by transaction cost minimization alone. Instead, poverty-oriented microfinance institutions could potentially deviate from their mission by extending larger loan sizes neither because of “progressive lending” nor because of “cross-subsidization” but because of the interplay between their own mission, the cost differentials between poor and unbanked wealthier clients, and region-specific clientele parameters. In a simple one-period framework we pin down the conditions under which mission drift can emerge. Our framework shows that there is a thin line between mission drift and cross subsidization, which in turn makes it difficult for empirical researchers to establish whether a microfinance institution has deviated from its poverty-reduction mission. This paper also suggests that institutions operating in regions which host a relatively small number of very poor individuals might be misleadingly perceived as deviating from their social objectives. Because existing empirical studies cannot differentiate between mission drift and cross-subsidization, these studies can potentially mislead donors and socially responsible investors pertaining to resource allocation across institutions offering financial services to the poor. The difficulty in separating cross-subsidization and mission drift is discussed in light of the contrasting experiences between microfinance institutions operating in Latin America and South Asia. © 2011 by World Scientific Publishing Co. Pte. Ltd. All rights reserved.
’smart subsidy’ might seem like a contradiction in terms to many microfinance experts. Worries about the dangers of excessive subsidization have been central to microfinance conversations since the movement first gained steam in the 1980s. From then on, the goal of serving the poor has been twinned with the goal of long-term financial self-sufficiency on the part of microbanks: aiming for profitability became part of what it means to practise good microfinance. The influential ‘Pink Book’, a newly reformulated set of ‘donor guidelines on good practice in microfinance’, for example, includes the idea that ‘microfinance can pay for itself, and must do so if it is to reach very large numbers of people. Unless microfinance providers charge enough to cover their costs, they will always be limited by the scarce and uncertain supply of subsidies from donors and governments.’1
This handbook, intended as a comprehensive source for donors, policy makers and practitioners, covers the policy, legal and regulatory issues relevant to microfinance development and examines the key elements in the process of building sustainable financial institutions with effective outreach to the poor. The handbook is divided into three parts: (1) issues to consider when providing microfinance - understanding the country context, the target market and impact analysis, products and services, and the institution; (2) designing and monitoring financial products and services - designing lending products, designing savings products and management information systems; and (3) measuring performance and managing viability - adjusting financial statements, performance indicators and performance management.
It has become common to try and increase the effectiveness of microfinance programmes by adding supplementary services to the financial product. However, the added value accruing from this ‘credit-plus’ approach has been little analysed. We hypothesise that the extent of added value from credit-plus depends on the ability of the credit supplier to cultivate trust, or social capital, amongst clients. Applying difference-in-difference estimation, we exploit a natural experiment of two ‘credit-plus’ programmes in Mexico. The findings suggest that credit-plus is not universally effective, but that it is at its most effective, especially with low-income groups, where ‘bonding’ (within-group) social capital exists.