Performance through Financial Ratios of South Asian Microfinance Institutions
Uzma Shahzad
Massey University - School of Economics and Finance
Hatice Ozer Balli
Massey University-School of Economics and Finance
Claire D. Matthews
Massey University - School of Economics & Finance
David W.L. Tripe
Massey University - School of Economics and Finance, Palmerston North and Wellington
Abstract
This study examines the performance of microfinance institutions (MFIs) using financial
ratios. These risks, cost and profitability ratios are measuring the dual objectives of MFIs i.e.
financial sustainability and outreach in the context of South Asian countries. The
performance is evaluated on the basis of datasets encompassed by 372 local MFIs’ activities
during 1998-2010. We find that by providing riskier and costly loans, MFI are actually
meeting their first objective of outreach more than the financial sustainability objective and
vice versa findings are with profitability ratios. Using random effect panel data estimation,
we find important ratios in context of performance measurement of MFIs and also conclude
that the trade-off between the dual objectives of MFIs is present.
Keywords: Microfinance Institutions, performance, financial ratios
JEL Classification: D02, E44, G21
1
1. Introduction
Microfinance1 is defined as the delivery of financial services, such as savings, loans and
financial insurance for low-income clients, including those who are self-employed such as
farmers (Ledgerwood, 1999). Microfinance is considered a vital and effective tool in the
global battle against poverty, but compared with traditional banks, it shows very high
intermediation margins (Krauss & Walter, 2009). Microfinance institutions (MFIs) work
similarly to conventional banks as they collect money (accept deposits) and make loans.
However, the difference is the target market as MFIs lend small amounts to the poor, accept
grants and generally have a lower default rates than conventional banks (Haq, 2008; Von
Pischke, 1996). However, in spite of these differences as they deal with money, their
financial and social performance still needs to be measured. In the past, microfinance simply
offered financial services to low income clients, but now it has broadened its scope to include
all those who are usually excluded by mainstream financial services. As the significance of
microfinance is growing, especially among donors and commercial parties, the requirement
for financial sustainability is becoming greater (Hardy, Holden, & Prokopenko, 2003).
The performance of MFIs can be measured by using the tools that are used to measure the
performance of traditional banks, but the bodies that grant money to the MFIs value the social
aspects more than the financial aspects (Weiss & Montgomery, 2007). Therefore in order to
undertake performance assessment of MFIs, both of these aspects needs to be addressed.
Moreover, as MFIs are a special form of financial institution that follow the dual objectives
of financial sustainability and social outreach2 so their performance also needs to be
measured according to these objectives (Cull, Demirguc-Kunt, & Morduch, 2007).
There is much debate among scholars as to whether the focus should be on a financial
perspective or a social perspective while assessing MFIs’ performance. At a broader level
these two concepts are perceived as both mutually compatible (Conning, 1999; Copestake,
2007; Edvardsen & Forsund, 2003; Woller, Dunford, & Woodworth, 1999) and conflicting
(Cull et al., 2007; Morduch, 2000). Although microfinance emerged four decades ago, the
question about institutions’ performance and productivity levels in terms of the dual
1
The term microfinance was used for the first time in the 1970s by Mohammad Yunus in Bangladesh and we will use it
throughout the text to describe the microfinance operations while MFIs will be used for referring to an organization.
2
Outreach is about providing financial services to more poor people and financial sustainability is about covering the cost of
these services. Outreach is generally measured in two dimensions – depth is measured by average loan balance per borrower
and gender of borrowers and scale is measured by number of active borrowers.
2
objectives is still unanswered and will be discussed in this study. We will examine both of
these objectives of MFIs and will try to determine their relationship with each other. We will
also examine whether this relationship differs across different types of ownership structures,
regulatory status and across various countries.
The primary mission of microfinance is to provide financial services to the poor but if they
don’t meet this primary objective, they are subject to call drifting away from their objective
or mission drift. According to Jones (2007) there are multiple sources to calculate the mission
drift. The topic of mission drift in microfinance has been studied by Cull et al. (2007),
Coperstake (2007), Mersland and Storm (2010), Armendariz and Szafarz (2011), Hermes
Lensink, and Meesters (2011). In the microfinance literature multiple items have been
measured as proxies for mission drift, although the most common is average loan size. Others
such as Mersland and Strom (2010) use lending methodology, borrower’s gender and MFI’s
main market as additional mission drift measures. Cull et al. (2007) use percentages of
women borrowers and average loan size as mission drift measures. Hermes et al. (2011) use
percentage of women borrowers, percentage of clients in bottom half of the population,
percentage of loans below US$300, average saving balance and average loan balance
measures in their study.
Gosh and Van Tassel (2008) suggest poverty gap ratio as the best approach to deal with
mission drift but at the same time they admit that in practice it is difficult to measure.
Average loan size is a justifiable mission drift proxy as Rosenberg (2009) remarks that if a
microfinance customers are asking for larger loans that shows the financial soundness of the
clients and it can be assumed that the customer is now turned into a middle class. He also
affirms in his study a reliable way to judge the mission drift is to look at the places where
MFIs are opening their new branches. Schreiner (2010) has created several poverty
scorecards that allows the categorization of the poor in different countries.
Several microfinance rating agencies have recently incorporated the social rating given the
importance of dual objectives of MFIs. To assess the mission drift in the evaluation process
Planet Rating has issued a social rating for MFIs. This has been done in a qualitative way that
is used in decisions such as customer diversification, branch opening and new products
development but a quantitative and comparable mission drift indicator is not available. In this
paper we have developed a different approach to measure the mission drift that is through
3
performance ratios. Back in 2003 a consensus group of MFI rating agencies, multilateral
banks, donors and private voluntary organizations agreed on some performance measurement
ratios3 for MFIs. This is the standard reference of C-GAP (2003) that is used to define
performance criteria for MFIs. According to Gutiérrez-Nieto, Serrano-Cinca, and Molinero
(2007) these ratios can also be used in performance measurement of institutions other than
MFIs. In this study we are using these ratios to assess the performance of South Asian4 MFIs.
In previous studies, different approaches have been used in performance assessment of MFIs.
For example, Yaron (1994) introduces an outreach and financial sustainability approach and
measures the performance of MFIs through efficiency. Farrington (2000) has applied
accounting ratios such as cost per borrower, return on assets, administrative expense ratio and
client per staff member to evaluate MFIs’ efficiency. Arsyad (2005) while measuring the
efficiency of Indonesian MFIs, take a similar approach in terms of cost per unit of currency
lent, operating cost ratios and cost per loan. To understand the relation between the
operational self-sustenance and financial self-sustenance, Crombrugghe, Tenikue, & Sureda
(2008) use regression analysis and find that there is no need for increasing the monitoring
costs of loans or size in order to meet the financing costs.
In terms of self-sustainability of MFIs, Morduch (2004) argues that the high rate of recovery
has somehow failed to transform the donor dependent microfinance industry into selfsustaining organizations. He contends that for financial sustainability of MFIs, along with
subsidies and external stakeholder’s support there is also a need to seek further financial
sources. Similarly, Crabb (2008) concludes, after analysing various MFIs, that external
stakeholders are important for the sustenance of these institutions. These stakeholders may
include government, societies, corporate bodies etc. Hartungi (2007) studies MFIs in
Indonesia and the various factors that are involved in the success of these institutions. The
major activities he identifies are usage of information technology in the outreach to the
people and dynamic adaption of MFIs to the local conditions. The study highlights that an
increase in transparency and active involvement of the MFI employees helped in better
functioning of MFIs in Indonesia.
3
In our process of choosing the more parsimonious variables, we include all of these ratios in earlier regressions but it does
not added any significant information so we are left with seven ratios for final analysis.
4
South Asian microfinance program has distinct characteristics and that is the reason for choosing this region. For example
South Asian region is the origin of microfinance and MFIs are largely concentrated in this region in comparison to the rest of
the world. Along with that according to the dataset used in this study, the South Asian microfinance program has shown a
consistent commitment to depositors and low income borrowers as a significant increase in these variables is present.
4
Pollinger, Outhwaite, & Cordero-Guzmán (2007) also highlight the need to explore further
external sources for raising new capital. Although in order to overcome the financial
sustainability issues governments provide different subsidies, these subsidies are not enough
for the long term sustenance of MFIs. Moxham and Boaden (2007) also find low utilization
for formal financial performance indicators of MFIs.
Navajas, Schreiner, Meyer, Gonzalez-Vega, & Rodriguez-Meza (2000) provides a theoretical
framework for the outreach of Bolivian MFIs and shows that MFIs are providing loans to the
richest among the poor. Kyereboah-Coleman (2007) highlights the importance of governance
in the MFIs and argues that in high risk exposures the outreach of MFIs increases due to
present debt to equity levels that are much higher compared to traditional times. Moxham
(2009) also tries to understand the application of performance indicators and finds good
acceptability of these indicators that are present in public, private and non-profit
organizations. Cull et al. (2007) study also uses the same logic of a financial sustainability
and outreach trade off in MFIs. Their study demonstrates that MFIs are losing their cause
of serving the poorest in order to generate profit.
Based on this review we conclude that none of the above studies explicitly measure the
performance of MFIs in terms of their objectives by using standard measures of financial
ratios suggested by C-GAP (2003). Some of the existing studies used these ratios but not in
relation to the dual objectives of MFIs. For example, Gutierrez-Nieto et al. (2007) use
profitability ratios but they suggest further investigation of the risk factor also in performance
assessment of MFIs. Some studies, for instance, Caudill, Gropper and Hartarska (2009) and
Paxton (2007) take into account more general efficiency determinants that are related to the
performance measurement in terms of efficiency analysis. Others like Arsyad (2005) used
these performance ratios just for making comparison among institutions and countries but no
evidence was found that these performance ratios have ever been used in comparisons of dual
objectives of MFIs.
Moreover, the review of microfinance literature highlights the importance of performance
assessment of MFIs and therefore we are addressing this topic briefly in terms of dual
objectives – outreach and financial sustainability of MFIs in this study. We will review the
impact of financial ratios i.e. portfolio quality, cost and profitability ratios on performance of
MFIs. We hypothesize that MFI financial sustainability is positively related to the
5
profitability and inversely related to the outreach while portfolio quality and cost ratios are
positively related to outreach and negatively related to financial sustainability. The
hypotheses are described below in detail.
1.1. Performance and portfolio quality ratios
Portfolio quality is generally measured with repayment risk that shows the riskier part of the
loan portfolio; the older the overdue default loans higher the chance of not being repaid
(Morduch, 1999). Credit quality has great impact on the performance so we use three
measures that will provide some assessment of credit quality of MFIs. The repayment rate is
one of the most important performance indicator and for MFIs, earning high profit margin
indicate their short term financial sustainability while high repayment rate is a necessary
condition of their long term financial viability (Ngo & Wahhaj, 2011). In this study, portfolio
quality is taken into account through loan repayment and includes portfolio at risk greater
than 30 days (PAR30), risk coverage and the write-off ratio (WOR) measures.
Portfolio quality ratios are hypothesized to be inversely related to the financial sustainability,
with higher ratios related to lower financial sustainability and positively related to outreach.
In support of this claim it can be said that more problem loans may indicate that the
institution is doing a better job with outreach, while fewer problem loans indicate less
outreach. Similarly with a small average loan size, all things being equal, problem loans will
be fewer in number. These are measures that help us to assess the portfolio quality and loan
repayment performance of MFIs clients so it is expected in this study that portfolio quality
ratios are positively related to outreach and inversely related to financial sustainability.
1.2. Performance and cost ratios
As productivity or efficiency ratios of MFIs provide the rate at which they are generating
revenues to cover their expenses so to measure the productivity of South Asian MFIs we are
using operating expense ratio (OER) and personnel allocation ratio in this context. It is
expected that a higher value of productivity ratios causes financial sustainability of MFIs to
be lower and outreach to be greater. The reason behind this argument is that a higher cost of
the institutions is good as they are spending to reach more poor clients. Thus it is expected in
this study that productivity ratios are inversely related to financial sustainability of the
institution and positively related to the outreach.
6
1.3. Performance and profitability ratios
Profitability plays a key role for the financial sustainability of an institution (Ledgerwood,
1999). In this study profitability is measured through funding expense ratio (FER) and capital
to asset ratio (CAR). It is expected that the higher the value of these ratios the more
sustainable will be an institution and the less will be the outreach. A high value of these ratios
is among the reasons that prevent formal financial institutions from providing credit services
to the poor. Therefore, it can be said that if an institution is achieving high value on these
indicators it is doing well on financial sustainability and at the same time not approaching the
real poor.
1.4. Performance and governance mechanisms of MFIs
Along with these financial ratios we also use some variables (as a benchmark model) that are
repeatedly reported in the performance assessment literature of MFIs. These variables include
institution specific and country specific variables that are described in detail in subsequent
section. In general, one would expect non- profit making MFIs, like NGOs, to achieve better
outreach in comparison to for-profit institutions. At the same time, for-profit institutions are
expected to show better financial performance in comparison to non-profit institutions.
Instead of age calculated from the business commencement date we created two dummies of
age according to the data that has alpha values of new, young and mature institutions.
As high economic growth result in expansion or contraction of microfinance services, on one
hand it may increase the profitable expansion opportunities and demand for microfinance
clients and on the other hand high economic growth may raises the household income at the
level that they are able to take part in formal financial services. Similarly inflation also
influences the performance since it increases lending cost, default rates and it may cause a
lowering of the real return of MFIs. Based on these evidences we assume that outreach and
self-sufficiency of MFIs is conditional on different economies, types of ownership and
regulations.
The hypotheses of this study are summarised as follow:
H1a: Portfolio quality/risk ratios are negatively related to financial sustainability.
H1b: Portfolio quality/risk ratios are positively related to outreach.
H2a: Cost ratios are negatively related to financial sustainability.
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H2b: Cost ratios are positively related to outreach.
H3a: Profitability ratios are positively related to financial sustainability.
H3b: Profitability ratios are negatively related to outreach.
H4: MFIs performance is conditional on the type of economy and governance structure of
these institutions.
The remainder of the paper is organised as follows: Section 2, the methodology and
diagnostic tests are provided. In section 3 discusses the description of data sets and variables
explanation is provided. In section 4, the regression results are presented, and according to
the formulated hypotheses, main findings are summarised in section 5.
2. Methodology
2.1. Model and diagnostic test
We have unbalanced panel data of 372 cross sections (MFIs) for 13 years from 1998 to 2010
with total 4,836 observations5. We used both absolute values and ratios for the purpose of
measuring the performance of MFIs and to control for circular arguments among variables
we have done some empirical tests. First of all because our data sample is annual we have
done a Granger Causality test (reported to second lag) that help us to determine the reasoning
of dependent and independent variables (Gujarati & Porter, 2003). To check the normality,
we use Jarque Bera statistics that appear to be high enough to reject the null hypothesis of
normality and as a remedy of non – normality we filtered some of the data variables and
delete the extreme values6. We also conduct the unit root test for each variable individually
and find unit root problem in some of the variables. As a remedy we take log values of those
variables (number of active borrowers and total assets) and after these alterations the unit root
problem does not exist in any of these estimators.
5
There are a large number of missing values for variables of interest and Eviews structure the workfile that is automatically adjusted for
194 cross sections and we are left with 586 observations. We want to check the trend analysis to look a picture of what happened to a typical
set of MFIs over time but for than we need consistent or balanced panel data that is not available to us therefore we use data from MFIs that
reported at any time from 1998 through 2010. Thus for example, a MFI that entered the market in 2002 or one that closed down in 2008
would be included in the data for the sample years when they provided reports. This approach of unbalanced panel data gave a better picture
of evolution of the whole market and thereby a better approximates the situation of microfinance clients and institutions over time.
6
Based on this it is concluded that our data is not normally distributed, outliers that come up in normality test as they are only a few
observations in our large data sample we simply delete the too high values that are not more than fifteen data points. We have to dealt with
them as outliers distract the overall regression results and it does not seem right to be in data.
8
We used fixed effects7 (FE) cross section as we were interested in analysing the trend (impact
of variables over time). Moreover, while trying both fixed and random effects alongside each
other, the test i.e. likelihood ratios and hausman test prove that fixed effect is better choice in
our model. To select the coefficient covariance methods we try each and every options and
find cross section-SUR as not suitable when number of cross sections is greater than number
of time periods and cross section and period weights can use only when diagonal elements
are used. We choose White cross section as a best coefficient covariance method in our case
because of the following
i)
It allow for general contemporary correlation between the cross sections (period
clustered)
ii)
Suitable when number of cross sections is greater than number of periods
We use generalized least square (GLS) model (Equation1) that is considered in panel data
estimation literature more efficient than OLS because of having smaller standard error and
best linear unbiased estimator in the presence of autocorrelation. In addition to that using
panel data over a long time period and its spatial dependencies on OLS estimators might
create autocorrelation, hetroskedasticity problem. GLS is considered more appropriate to
address such shortcomings8 (Lee, 2005).
𝑃𝑖𝑗𝑡 = 𝛽0 + 𝛽1 𝑅𝑖𝑗𝑡 + 𝛽2 𝑆𝑖𝑗𝑡 + 𝛽3 𝑀𝑖𝑗𝑡 + 𝜀𝑖𝑗𝑡
(1)
This model represents multiple equations in one regression model. In each equation Pijt
represents the performance of MFI i located in country j in year t. The performance of MFIs
is measured by the variables of outreach and financial sustainability.
Outreach is measured by number of active borrowers (NAB), average loan balance per
borrower / gross national income (ALBPBG) and per cent of female borrowers (PFB) and
7
It explores the link between the predictor and other outcome variables but as each institution has its own individual characteristics that
may or may not influence the predictor variables so while using fixed effect it is assumed that something within the individuals may impact
any of these variables that needs to be controlled. This is the rationale behind the assumption of the correlation between predictor variables
and individual’s error term that fixed effect removes the effect of those time-invariant characteristics from the predictor variables and we
can assess the predictors’ net effect. Another assumption of fixed effect model is that those time-invariant characteristics are unique and
should not be correlated with other individual characteristics as each entity is different therefore the entity’s constant term and error term
which captures individual characteristics should not be correlated with one another. Moreover, The GLS model allows us to test for time
constant variables and most prominent in our case is the MFI’s institutional type. Fixed effect model is used and to check the validity of this
proportion, a Hausman test and Likelihood ratio test are conducted that indicates the fixed effect model assumptions do hold.
8
GLS applies the clustering technique to correct heteroskedasticity and uses the Cochrane–Orcutt procedure to correct the autocorrelation
of the error terms. In so doing, it is assumed that the error terms follow an AR (1) process for each country.
9
financial sustainability is measured by return on assets (ROA) and operational selfsufficiency (OSS). Rijt represents financial ratios of MFIs categorized by risk, cost and
profitability ratios. Sijt is vector of MFI specific variables: regulatory status; age of institution;
total assets and ownership structure of each institution in each particular country in terms of
its types of ownership. Mijt represents the country-specific macroeconomic variables. These
variables include size of the economy of the country that will be represented by GDP;
inflation and human development index. Finally, 𝜺𝒊𝒋𝒕 is the error term and is assumed to be
independent and normally distributed with a zero mean.
2.2. Benchmark model and dependant variables
Existing literature on microfinance argues that MFIs’ specific variables and country specific
variables affect the MFIs’ performance (Ahlin, Lin, & Maio, 2011; Hermes et al., 2011).
Therefore, in additional to financial ratios these variables are added to control for any factors
or differences that are present in institutions and countries and we include these variables in
our analysis as a benchmark model or control variables.
Through this benchmark model we may find the robustness of our results aligning with the
existing literature. MFI specific variables are: MFI size, measured by the total assets; MFI
age and MFI types. Our data sets show five different types of MFIs operating in South Asian
countries that are NGOs, NBFIs, Credit unions, Banks and Others. As there are too many
categories to be discussed individually we have divided them in two categories as suggested
by Quayes (2012) but we have used different terminologies which we think as more
appropriate. We divided these institutions in two categories that are non-profit making
institutions (NPIs) and profit making institutions (PIs)9. To include them in one regression
model we create dummy of NPIs and PIs with latter being the omitted dummy in the
regression analysis. Dummy ‘dyoung’ that include both new and young institutions (less than
8 years old) put as a base dummy and ‘dmature’ that include greater than 8 years old
institutions is included in regression model.
The country level control variables are those macroeconomic variables that are identified by
the microfinance literature as possible determinants for assessing the MFIs performance.
Country level data is downloaded from World Bank Development Indicators that includes the
9
NPIs include NGOs, credit unions and other institutions and the category of PIs include banks, rural banks and NBFIs.
10
common measure variables of financial development proposed in the finance and growth
literature. The variables are inflation, Human Development Index (HDI) and GDP levels of
each sample country. Nominal GDP (in current US billion dollars) is used as a proxy to
measure the size of economy but instead we have calculated the growth of GDP indicator as
suggested by Ahlin et al. (2011). Growth in GDP is calculated by using the equation 2.
𝐺𝑟𝑜𝑤𝑡ℎ 𝑖𝑛 𝐺𝐷𝑃 = log 𝐺𝐷𝑃𝑡 − log 𝐺𝐷𝑃𝑡−1
(2)
According to Ahlin et al. (2011), inflation can hinder the microfinance lending mission and
may also impact on microfinance cost of funds and borrowers incentives for default and
delays. Moreover unanticipated inflation lowers the MFIs’ returns and in response MFIs may
build (conservatively) large inflation premia into interest rates.
Instead of using national income per capita we used HDI as the metric of development
success. The maximum and minimum values of HDI values show that firms came from
different wide variety of country background and this indicator is also expected to capture
some of their institutional differences.
Moreover, dependant variables used to measure the outreach and financial sustainability are
as follows. Firstly, outreach of MFIs can be measured by breadth and depth of outreach.
Breadth of outreach is considered as a quantity of outreach and depth of outreach is
considered as a quality measure of microfinance credit (Ahlin et al., 2011; Balkenhol, 2007;
Christen & Drake, 2001; Hermes et al., 2011). Breadth or scale of outreach is measured by
the number of active borrowers and depth of outreach or poverty level of microfinance clients
is measured by percentage of women borrowers and average loan balance per borrower/GNI.
We measure the breadth of outreach and instead of using number of active borrowers we
normalise it by dividing it with total number of borrowers group by every country. We also
check our results with depth of outreach (percent of female borrowers and average loan
balance per borrower/GNI) using as dependant variables of outreach indicators but find that
they yield almost similar results that are consistent with of full sample results presented in
table 4. Although in case of percent of female borrowers we could not yield most of the
significant results may be because we have less number of observations in this indicator.
11
Secondly, financial Sustainability of an institution is among one of the factors that determine
its progress and is also about generating enough revenues from financial services to cover
operational and financial cost. We use ROA and OSS in this context. ROA is a measure of
overall profitability of an institution (Galema, Lensink, & Spierdijik, 2011). OSS is a
financial performance indicator to measure the ability of a MFI to cover its costs through
operating revenues. OSS is considered to be a direct measure of the institutions’ financial
sustainability that refers to generating enough revenues to cover all of its financial and
operational cost (Quayes, 2012). We use both of these indicators but for brevity final results
are reported for OSS indicator only.
3. Description of data
The annual data10 is gathered from various sources; primary data on MFIs is downloaded
from Microfinance Information exchange (MIX) market11 and macroeconomic data is
downloaded from the World Bank website12. Data related to financial statements and other
relevant information is also gathered from MIX market. Given that MFI data is downloaded
from MIX market and definition of the variables (summarised in Table 1) are also utilized
from MIX given information.
[INSERT TABLE 1 ABOUT HERE]
Our dataset comprises MFIs operating in five countries of South Asian region, (Bangladesh,
India, Nepal, Pakistan and Sri Lanka). These MFIs are categorized as follows: 22 MFIs in the
sample are banks (6%), 41 are credit unions/cooperatives (11%), 89 (24%) are non-bank
financial institutions (NBFIs), 205 (55%) are non-government organizations (NGOs) and 15
institutions are categorized as ‘other’.
[INSERT TABLE 2 ABOUT HERE]
Table 213 shows the summary of some of the descriptive statistics and from these statistics, it
can be observed that the performance of MFIs is widely spread. The average age for MFIs is
about 13 years, although one MFI can trace its activities back to 1965. The number of
microfinance institutions operating in India and Bangladesh represent 50% and 21%
10
However because country-specific indicators are often reported only once in a year so they are assumed to be constant over the whole
period of one year.
11
The primary data is retrieved from MIX market during November/December 2012 from http://www.mixmarket.org/.
12
See http://data.worldbank.org/indicator
13
For brevity we are not including the descriptive statistics of sub-sample data but it can be available on request.
12
respectively of our sample, while Nepal, Pakistan and Sri Lanka represent 13%, 8% and 7%
respectively. Regulated institutions represent 63% of the sample indicating that regulated
institutions are more common than non-regulated institutions. The minimum (0.40) and
maximum (0.69) values of HDI indicator show that the institutions come from a wide variety
of backgrounds; some of their institutional differences may be captured through country
specific institutions.
[INSERT TABLE 3 ABOUT HERE]
Correlations among variables are presented in Table 3. This correlation matrix is constructed
for each set of data to identify the basic relationship among regressors and to explore the
potential of multi-collinearity. High correlations between the two variables indicate that both
represent the same concept and is not desirable to include both in the same model. After
reviewing the correlations (the benchmark value is above 0.70) among the variables it can be
said that the data do not have the multi-collinearity problem14. In addition to that since panel
data estimates gave more data points; the multi-collinearity problem is hence reduced even
further (Hsiao, 2003).
4. Interpretation of results
We precede this discussion with the regression results that are presented in Tables 4 and 5.
These regression results are from estimation of outreach and financial sustainability
indicators. The model covers all explanatory variables from Table 1. We comment on all
regression results together15. Although most of the signs of coefficients are as expected, not
all of them are significant. However, there are some interesting results that warrant
discussion. We report the empirical results in this section and discus how firm specific and
country specific variables may affect the MFIs performance in terms of financial
sustainability and outreach.
[INSERT TABLE 4 ABOUT HERE]
4.1. Outreach
14
The correlation problem occurs when correlation among indicators are strong enough to simultaneous include them in
same regression model create misleading results.
15
For brevity, all results discussed in text are not reported in tables. These unreported results can be available from the
authors upon request.
13
Table 4 presents the regression results of outreach that is measured by number of active
borrowers divided by total number of active borrowers grouped by each country respectively.
We reported the MFIs’ specific characteristics and country specific characteristics regression
results before financial ratios so they will be explained accordingly.
In MFIs’ specific characteristics we find outreach is positively related to the size of MFI that
shows larger institutions have greater outreach. We haven’t got enough older institution to
generate meaningful distinction therefore no conclusion can be made on age indicator as it
has insignificant coefficients for all outreach measures. It probably indicate the number of
young institution in microfinance industry are high in number or new firms have newer
technologies that gave them an advantage relative to the older institution; these results are
consistent with the findings of Hartarska (2005) and Hudon (2010).
Increase in GDP indicator shows the positive relation with outreach so we may conclude that
higher GDP drive better outreach of the institutions. Overall effect of GDP growth is less
prominent in outreach indicators comparable results were found in Bassem (2009) and Ashraf
and Hassan (2011). We find country specific characteristics get significant results in outreach
measure. Inflation has a negative impact on outreach that is contrary to the findings of
Hartarska (2005) and Bassem (2009). These results are internally consistent as we find that
the size of economy affects the dual objectives of MFIs. The negative values also indicate in
a highly inflationary environment to prevent MFIs to reach more borrowers. The living
standard measure shows an insignificant coefficient of outreach.
While introducing financial ratios in the benchmark model we find quite consistent results in
all categories of risk, cost and profitability ratios. No evidence has been found that these
ratios have been tested according to the dual objectives so we cannot compare the results with
other studies. But the control variables of institutional specific and country specific
characteristics results are compared with existing studies and these results not only explain
the dual objectives of MFIs but also show trade-off between these objectives.
In hypotheses we expect risk and cost ratios are compatible with outreach – higher value of
these ratios higher the higher outreach of the institution will be. Among risk ratios, PAR30
and risk coverage ratios show a positive link with social outreach that is according to our
expectations but write-off ratio shows negative link with social outreach. It might indicate the
14
nature of financial institutions more than non-financial institutions who concentrate more on
financial sustainability. This is tested and third column of table 6 confirm it.
Among cost ratios we find significant results in operating expense ratio that has a positive
impact on outreach efforts of MFIs and has 5% significant level coefficient that indicate
association of high cost with smaller sized loans. Operating expense is higher relative to
assets while measuring outreach with number of active borrowers that probably shows that
South Asian MFIs are not much efficient in serving more borrowers and these findings are
contrary to Hermes et. al. (2011) and with Cull et. al. (2007) who conclude that scale of
outreach is associated with lower average cost. It is probably got to do with other factors as
well that might give some more insights while measuring the DEA that is exactly according
to our expectations that we made based in the existing literature but we hope to get much
better understanding about it while using other efficiency measures like DEA and SFA using
same variables.
Among profitability ratios, CAR comes up according to expectations and shows a negative
significant coefficient. Higher value of funding expense ratio indicates higher interest rate
(varying across countries and across times based on its distribution) that is an indicator that
MFIs are performing better job and it also reflects the nature of their business. Those that are
paying more funding cost are better managed than those that are have low funding cost
because of subsidies it is assumed that they are not operating efficiently. This is something
that is worth of further exploration and we expect to get some insights into that in DEA
context.
[INSERT TABLE 5 ABOUT HERE]
4.2. Financial Sustainability
Table 5 present the regression results of financial sustainability of MFIs using operational
self-sufficiency as a dependent variable. We summarise the results in terms of impacts of the
firm levels and country specific variables on the selected financial sustainability measure.
Examining the control variables, we find insignificant effect of all control variables when all
financial ratios are included in the model otherwise log of assets, growth in GDP and
inflation appear as significant in most earlier regressions reported in 1-11 columns of table 5.
We only explain the results appear in the last column of the table 5. The control variables
findings are contrary to the Cull et. al., (2007) and Mersland and Strom (2009a). We take
15
total assets and age of the firm as a proxy for the size of MFIs; natural logarithm of total
assets has a positive and significant impact on performance (both financial sustainability and
outreach) but age does not and in contrast to Hartarska (2005) and Bassem (2009) who found
positive impact of age on financial sustainability of MFIs.
Among financial ratios we find consistent significant results and in most cases they appear as
according to our expectations. Among portfolio quality ratios, portfolio at risk and write off
ratios have negative coefficients, exactly as was expected in first hypothesis - we proposed
that riskier loans are not favourable for the financial sustainability of MFIs so after getting
the regression results it can be said that higher the value of risk ratios, lower the value of
financial sustainability. Looking at the cost ratios, results render negative relationship with
financial sustainability at 1% significance level and that is exactly as we are expecting higher the value of cost ratios, lower will be the financial sustainability of the institution and
vice versa. Operating expense ratio shows the results among other cost ratios with highly
significant coefficients – higher the value of operating expense ratios lower will be the value
of financial sustainability.
Similarly among profitability ratios, capital to assets ratio shows the positive results that is
again appear according to our proposed hypothesis that is higher the value of profitability
ratios, higher will be institution financial sustainability. The size of a MFI is expected to have
a positive association with the financial sustainability and same is true for level of total
equity. So it can be argued that greater equity may have a positive impact on the financial
performance of an MFI, these results are in contrast to Quayes (2012). Based on this we
might also say that better capitalized MFIs reflect higher management quality and thereby
enhance profitability.
[INSERT TABLE 6 ABOUT HERE]
Instead of introducing dummies we categorize the data as financial and non-financial
institutions, regulated and non-regulated institutions and India’s (51% institutions are from
India) and other countries institutions. As Bert et. al. (2011) and Mersland and Strom
(2009a) also convince that performance of MFIs varies in their different types of ownership
structure therefore we split our data sample to check the robustness of our results and carry
out additional regressions (table 6) using various alternative specifications and composition
of MFIs sample for each of corporate governance components such as financial and non16
financial institutions, Indian and other sample countries institutions, regulated and nonregulated institutions and for overall datasets and find almost qualitatively consistent
results16. Overall we conclude that our results are quite consistent with reported results in
several specifications.
5. Summary and conclusion
This study documents the performance assessment of MFIs through financial ratios after
controlling for the institution and economics determinants. These financial ratios are
suggested by a consensus group of rating agencies, banks, donors and voluntary organizations
(C-GAP, 2003) for performance measurement of MFIs. This paper has used a GLS model
using a sample of 372 MFIs. Across specifications, the MFIs size affects the outreach and
financial performance positively as coefficient values are positive and highly significant,
indicating, that growth in size of the institution positively affects the performance.
Providing financial services to the poor people while being financially sustainable are dual
objectives of microfinance and in this study we try to attempt whether these objectives are
compatible or contrary to each other and based on our hypotheses we conclude that there is a
trade-off between both objectives of microfinance. But some measures can be taken to
overcome the conflict like increasing the loan size or assets size; hence with the passage of
time they can be able to enjoy the economies of scale. Moreover when we group the different
types of MFIs in two sub-groups as NPIs and PIs and we get same consistent results in terms
of financial sustainability and outreach. For comparisons we are including both sets of results
in the end to make a better comparison in table 6.
The empirical evidence shows the difficulty of achieving the dual objectives of MFIs
simultaneously. In practice, the microfinance program often entails distinct trade-offs
between maximizing the financial performance and meeting social goals and evidences
suggest that the trade-off between the two is existent. These results are consistent with
Hermes et al. (2011) who posit that aiming for MFIs on financial sustainability means
compromising on their social goals. Similarly Cull et al. (2011) describe that transformation
of MFIs into formalized banking institutions has no positive effect for the poor. This
16
We are not reporting these separate tables and for the brevity we just gave one regression table but all
other regression results that are discussed in the paper can be available on request.
17
provocative message is clear for all stakeholders of MFIs. For example, it is relevant for
policy makers in making decisions of microfinance subsidization. Furthermore it is relevant
for commercial investors, especially those who are aiming for socially responsible
investments and also for those microfinance practitioners who make decisions for
improvement in the efficiency of their operations.
Although our preliminary objective is to measure the performance of South Asian MFIs
while using the financial ratios and get some insights in trade-off of microfinance objectives
but we can’t solve everything in present study and expected the trade-off better done in DEA
context. In this paper, the research revisits the traditional argument of trade-off or mutuality
between MFIs financial self-sufficiency and reaching poor clients. The overall conclusion is
that few of the financial ratios describe the dual objectives of MFIs and trade-off between
them but not all. Several elements of the study findings are puzzling that motivate for future
research work. We suggest the following. Firstly, the present study is a basic study for
performance measurement of MFIs, more sophisticated techniques such as data envelopment
analysis (DEA) and stochastic frontier analysis (SFA) are required to check the robustness of
these results. We intend to further investigate these regression results with these sophisticated
techniques. Secondly, the assessment of performance is required in terms of current legal
structure of these institutions in detail. For example which type of institution (and in which
country) is most efficient in terms of outreach and financial sustainability, what type of
lending methodology is most appropriate and what are the other success factors that can be
used as a benchmark in microfinance industry.
18
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21
Table 1: Variables descriptions
Name
Number of active borrowers
Outreach
Dependant
variables
Average Loan Balance per
Borrower GNI (%)
Number of active borrowers
divided by total borrowers in
each country
Percentage of Female Borrowers
(%)
Return on assets (%)
Calculated as
Log values of total borrowers and
clients of MFIs
Normalized by gross national income
Explanation
The number of credit clients at the end of each
period
Outreach indicator
Normalized
by
dividing
borrowers per country
total
Outreach indicator
Number of female borrowers divided
by total clients
(Net operating income minus taxes)/
divided by average assets
Outreach indicator
Sustainability
Operational self-sufficiency (%)
age
Institution control
size
Institution’s type
Regulatory status
Growth in GDP
country control
HDI
Inflation
Portfolio at risk >30 days (%)
Risk coverage ratio (%)
Risk Ratios
Write-off ratio (%)
Operating expense ratio (%)
Cost Ratios
Personnel Allocation Ratio (%)
Financial
revenue
divided
by
(financial expense + impairment loss +
operating expense)
Year of experience as an MFI
The natural logarithm of total assets
Profitable
and
non-profitable
institutions
Regulated
and
non-regulated
institutions
Size of the economy of the country in
current US dollars in billions.
Composite country index covering
education, life expectancy and income.
Portfolio at Risk > 30 days divided by
gross loan portfolio
Impairment loss allowance divided by
PAR > 30 days
Write offs divided by average gross
loan portfolio
Operating expense divided by average
gross loan portfolio
Loan officers divided by personnel
22
It measures the potential ability of a MFI to
generate a commercially accepted return that
can enable it to become a formal financial
institution, with the opportunity to access
commercial financing.
It measures how well MFI can cover its
operating cost through its operating revenues.
Data source
MIX market
Dyoung (<8 years old) and dmature (>8years
old) dummies are created for age and put
dyoung dummy as base dummy.
Proxy of size measure
Different ownership structure of MFIs
Different regulatory structure of MFIs
Proxy to measure the country size
Proxy of quality of life
Inflation, consumer prices (annual %)
Portfolio at risk > 30 days divided by gross loan
portfolio. The ratio shows the value of
outstanding loans that are due more than 30
days.
Risk coverage ratio is equal to loan loss
reserves divided by portfolio at risk. According
to MIX market this ratio is named as risk
coverage ratio that is calculated by Impairment
loss allowance divided by PAR > 30 days
Value of loan written off divided by average
gross loan portfolio
Cost ratio
Cost ratio
World Bank
Development Indicators
MIX market
Profitability Ratios
Funding expense ratio (%)
Financial expense divided by average
assets
Total Equity divided by Total Assets
Capital to assets ratio (%)
Profitability ratio
Profitability ratio
Table 2: Descriptive statistics of Variables
Outreach
All data
Mean
Average loan balance per
borrower / GNI
Number of active borrowers
Percentage of female borrowers
Number of active borrowers /
assets
AGE
Dummy of young institutions
Number of active borrowers /
total borrowers in each country
Return on assets
Operational self-sufficiency
(N = 576)
Maximum
Minimum
Std. Dev.
Sustainability
All data
(N = 589)
Mean
Maximum
Minimum
Std. Dev.
0.216
6.357
0.009
0.329
315817
6430000
565
935737
0.862
1.041
0.026
0.241
0.007
13.036
0.073
51.000
0.000
1.000
0.005
8.755
13.005
51.000
1.000
8.698
0.288
1.000
0.000
0.453
0.287
1.000
0.000
0.453
0.005
0.061
0.000
0.009
0.005
1.124
0.308
3.357
-0.972
0.179
0.076
0.304
Dummy of mature institutions
Assets
Growth in GDP
0.712
51664338
6.757
1.000
1410000000
9.801
0.000
132970
1.596
0.453
145000000
2.283
0.713
51041214
6.710
1.000
1410000000
9.801
0.000
132970
1.596
0.453
143000000
2.295
Human development indicator
Inflation
Portfolio at risk > 30 days
Risk coverage ratio
Write off ratio
Operating expense ratio
Personnel allocation ratio
Funding expense ratio
Capital to asset ratio
0.513
9.254
0.065
2.799
0.010
0.166
0.595
0.063
0.178
0.686
22.564
0.994
80.507
0.247
1.871
1.225
0.286
0.985
0.424
3.418
0.000
0.000
-0.001
0.009
0.014
0.000
-0.482
0.053
3.466
0.129
7.575
0.027
0.158
0.186
0.031
0.174
0.513
9.336
0.068
2.759
0.010
0.166
0.593
0.063
0.179
0.686
22.564
1.000
80.507
0.247
1.871
1.225
0.286
0.985
0.424
3.418
0.000
0.000
-0.001
0.009
0.014
0.000
-0.482
0.054
3.541
0.135
7.498
0.027
0.158
0.188
0.031
0.176
Table 3: Correlation
23
DYOUNG
Dummy of young
institutions
Dummy of mature
institutions
Percentage of female
borrowers
Number of active
borrowers
Number of active
borrowers / assets
Number of active
borrowers / total
borrowers in each
country
Average loan balance
per borrower / GNI
Return on assets
Operational selfsufficiency
AGE
Assets
Growth in GDP
Human developmnet
indicator
DMATURE
PFB
NAB
NAB/AST
NABDTNAB
ALBPBG
ROA
OSS
AGE
AST
1
0.240
-0.115
1
0.037
-0.154
0.011
-0.069
-0.025
0.141
0.029
GGDP
HDI
INFL
PAR30
RSKC
WOR
1
OER
PAR
FER
1
0.009
0.052
1
0.312
CAR
1
-1.000
1
-0.034
0.034
1
-0.136
0.136
0.063
1
0.013
-0.013
0.283
0.051
1
-0.122
0.122
-0.131
0.641
-0.127
1
-0.262
0.105
-0.010
0.099
1
0.019
1
0.090
0.081
-0.032
0.163
0.159
0.108
0.666
-0.195
0.051
0.030
-0.041
-0.151
0.766
0.172
0.120
0.044
1
0.153
0.183
0.059
-0.033
0.049
-0.243
-0.183
0.098
0.003
0.007
0.039
-0.078
0.014
0.019
0.010
0.058
0.118
0.023
0.080
0.179
0.047
-0.017
-0.007
-0.044
0.030
-0.047
-0.074
0.032
-0.100
-0.109
-0.100
0.067
0.293
0.509
-0.044
-0.118
0.083
0.378
0.715
-0.214
-0.106
0.005
0.095
0.082
0.106
-0.180
0.045
-0.179
-0.008
-0.131
-0.179
0.131
0.179
-0.260
0.227
0.056
0.146
-0.177
-0.648
-0.140
0.104
0.177
0.648
0.140
-0.104
0.201
0.050
0.025
0.136
0.232
0.282
0.946
0.028
0.196
-0.196
-0.108
0.040
0.055
Inflation
Portfolio at risk > 30
days
0.098
-0.098
-0.151
-0.139
0.139
-0.065
Risk coverage ratio
-0.009
0.009
0.085
Write off ratio
Operating expense
ratio
Personnel allocation
ratio
0.078
-0.078
-0.268
0.313
-0.313
-0.260
0.019
0.108
0.086
-0.086
0.236
0.065
0.143
-0.042
-0.202
0.015
Funding expense ratio
0.046
-0.046
0.170
0.043
0.009
0.030
-0.079
0.044
0.011
0.015
Capital to asset ratio
0.161
-0.161
-0.238
0.118
0.006
0.005
-0.072
0.091
0.114
Table 4: Outreach
24
1
0.281
0.197
1
0.168
1
0.041
0.067
0.053
0.013
-0.151
0.180
0.261
0.123
0.210
0.012
0.139
0.041
0.011
0.132
1
0.090
0.044
-0.126
0.042
0.091
0.246
0.249
0.164
-0.037
0.071
0.012
0.100
0.035
0.003
0.103
0.016
1
0.390
0.034
0.074
1
0.046
0.174
0.163
0.245
1
Dummy of mature
institutions (>8 years)
Log of assets
Growth in GDP
Inflation
Human Development
Indicator
Benchmark
model
0.001
(0.677)
0.001***
(3.238)
0.000
(-1.119)
0.000*
(1.983)
Risk ratios
Cost ratios
0.001
(0.591)
0.002***
(3.600)
-0.000*
(-2.007)
0.000
(1.481)
0.000
(-0.277)
0.002***
(4.082)
-0.000*
(-1.840)
0.000*
(1.768)
0.000
(0.422)
0.002***
(3.698)
0.000
(-0.828)
0.000*
(1.994)
0.000
(-0.350)
0.002***
(4.194)
-0.000**
(-2.250)
0.000*
(1.699)
0.000
(0.365)
0.002***
(5.343)
0.000
(-1.036)
0.000**
(2.307)
0.001
(1.177)
0.002***
(3.466)
-0.000**
(-2.126)
0.000**
(2.075)
0.001
(0.909)
0.003***
(6.912)
-0.000*
(-1.646)
0.000**
(2.132)
0.001
(0.539)
0.002***
(3.282)
0.000
(-0.629)
0.000**
(2.314)
0.001
(0.645)
0.001***
(3.249)
0.000
(-0.918)
0.000**
(2.011)
0.001
(0.560)
0.002***
(3.278)
0.000
(-0.612)
0.000**
(2.321)
0.055***
(3.962)
0.042***
(2.842)
0.024
(0.925)
0.045***
(3.131)
0.025
(0.928)
0.029**
(2.119)
0.020
(0.621)
-0.015
(-0.558)
0.034**
(2.357)
0.052***
(3.926)
0.034**
(2.375)
0.002*
(1.908)
Portfolio at risk >30 days
-0.002
(-0.242)
Write off ratio
All data
With
selected
ratios
0.001
(1.601)
0.004***
(7.198)
0.000*
(-1.651)
0.000
(1.41)
0.064***
(-2.941)
0.003***
(4.105)
0.000*
(-1.764)
0.008***
(-2.832)
0.003*
(1.940)
0.000
(-1.236)
0.000
(-1.592)
Risk coverage
Profitability ratios
-0.001
(-0.247)
0.001**
(2.598)
operating expense ratio
-0.003*
(-1.831)
Personnel allocation
ratio
0.003**
(2.081)
-0.002
(-1.178)
0.002***
(5.003)
-0.003
(-1.582)
-0.001
(-0.487)
0.018
(1.591)
0.000
(-0.115)
0.039**
(2.802)
-0.004*
(-1.947)
0.018*
(1.695)
Funding expense ratio
Capital to assets ratio
R-squared
0.88
0.90
0.88
0.88
0.88
0.89
0.90
0.90
0.89
0.88
0.89
0.898
Cross sections
318
301
257
287
243
300
236
226
297
318
297
194
586
Observations
1159
1006
823
907
737
1003
809
729
985
1156
985
Table 4 presents the regression results of outreach that is measured by number of active borrowers divided by total number of active borrowers grouped by each country respectively. We reported the MFIs’ specific
characteristics and country specific characteristics regression results before financial ratios so they will be explained accordingly. First column demonstrate the benchmark model only indicators and in subsequent
columns financial ratios are included one by one. In last column on top of benchmark mark model we include one important financial ratio from each category.
25
Table 5: Financial sustainability
Dummy of mature
institutions (>8
years)
Log of assets
Growth in GDP
Inflation
Human
Development
Indicator
Benchmark
model
0.036
0.029
0.046
0.047
0.061
0.052
-0.031
-0.018
0.047
0.035
0.045
0.025
(0.848)
0.051**
(2.324)
-0.006
(-1.118)
-0.001
(-0.382)
-1.570
(0.707)
0.041**
(2.013)
-0.005
(-0.996)
0.001
(0.230)
-0.898
(1.175)
0.070***
(3.001)
-0.008*
(-1.627)
0.000
(-0.029)
-2.258
(1.007)
0.051**
(2.271)
-0.009
(-1.603)
0.000
(0.039)
-1.693
(1.363)
0.064**
(2.401)
-0.007
(-1.210)
0.000
(-0.023)
-1.782
(1.205)
0.033
(1.554)
-0.006
(-1.268)
-0.002
(-0.588)
-1.515
(-0.964)
0.119***
(9.082)
-0.005
(-1.557)
-0.001
(-0.260)
-3.679**
(-0.684)
0.097***
(5.051)
-0.005*
(-1.634)
-0.001
(-0.221)
-3.391*
(1.083)
0.055**
(2.734)
-0.010
(-1.535)
-0.002
(-0.426)
-1.069
(0.779)
0.053**
(2.296)
-0.006
(-1.013)
-0.001
(-0.349)
-1.630
(0.959)
0.056**
(2.642)
-0.010
(-1.465)
-0.002
(-0.417)
-1.120
(0.647)
(-1.247)
(-0.705)
(-1.241)
(-1.143)
(-0.803)
(-1.181)
(-2.368)
(-1.996)
(-0.765)
(-1.333)
(-0.832)
Portfolio at risk
>30 days
Risk ratios
Cost ratios
-0.391***
(-5.969)
1.339***
(-3.378)
Write off ratio
All data
0.012
(0.915)
-0.004
(-1.026)
0.001
(0.158)
0.535
(0.216)
-0.851**
-1.077***
(-2.833)
operating expense
ratio
With
selected
ratios
0.436***
(-4.962)
-0.001
(-0.831)
-0.464***
(-6.173)
-0.004
(-1.464)
-0.002
(-0.822)
Risk coverage
Profitability ratios
(-2.544)
-0.257***
(-5.446)
Personnel
allocation ratio
0.087
(0.977)
0.555***
(-3.759)
0.020
(0.198)
-0.125**
(-2.678)
0.068
(0.769)
Funding expense
ratio
-1.227**
(-2.756)
0.083
(0.664)
Capital to assets
ratio
-1.184**
(-2.462)
0.073
(0.529)
-0.369
(-0.508)
0.531**
(3.119)
0.753
198
589
Observations
1168
1007
822
922
742
1028
780
730
1009
1168
1009
Table 5 present the regression results of financial sustainability of MFIs using operational self-sufficiency as a dependent variable. We summarise the results in terms of impacts of the firm levels and country specific
variables on the selected financial sustainability measures. First column demonstrate the benchmark model only indicators and in subsequent columns financial ratios are included one by one. In last column on top of
benchmark mark model we include one important financial ratio from each category.
R-squared
Cross-sections
0.73
318
0.74
301
0.80
258
0.73
290
0.82
246
0.74
304
26
0.71
234
0.72
228
0.73
301
0.73
318
0.73
301
Table 6: Sub sample regression results
(I)
All data
(II)
Financial institutions
(III)
Non-financial institutions
(IV)
Indian institutions
Outreach
Capital to assets ratio
0.002*
(1.693)
0.003***
(6.663)
0.000*
(-1.818)
0.000
(0.262)
-0.056***
(-3.982)
0.001
(1.289)
0.000
(-1.640)
-0.005
(-0.965)
0.000
(-0.275)
-0.002*
(-1.717)
0.006
(0.555)
-0.004
Sustainabi
lity
0.052
(1.088)
-0.003
(-0.089)
-0.007
(-0.924)
-0.004
(-0.549)
0.377
(0.152)
-0.579***
(-2.763)
-0.001
(-0.700)
-0.897**
(-2.072)
-0.955***
(-4.187)
-0.078
(-0.435)
-0.369
(-0.343)
0.879***
Outreach
0.001**
(2.538)
0.004***
(4.030)
0.000
(-0.805)
0.000*
(1.715)
-0.052
(-1.532)
0.006*
(2.590)
0.000
(0.089)
-0.021*
(-1.863)
0.007***
(2.797)
-0.001
(-0.676)
0.060***
(3.469)
-0.002
Sustainabi
lity
0.042
(0.759)
0.063
(1.514)
-0.003
(-0.942)
0.004
(0.589)
-0.906
(-0.229)
-0.326***
(-5.181)
0.001
(0.748)
-0.067
(-0.153)
-0.183
(-1.327)
0.160**
(2.481)
-0.842
(-0.619)
0.247
Outreach
0.001
(1.601)
0.004***
(7.198)
0.000*
(-1.651)
0.000
(1.41)
-0.064***
(-2.941)
0.003***
(4.105)
0.000*
(-1.764)
-0.008***
(-2.832)
0.003**
(2.081)
-0.002
(-1.178)
0.039**
(2.802)
-0.004*
Sustainabi
lity
0.025
(0.647)
0.012
(0.915)
-0.004
(-1.026)
0.001
(0.158)
0.535
(0.216)
-0.436***
(-4.962)
-0.001
(-0.831)
-0.851**
(-2.544)
-0.555***
(-3.759)
0.020
(0.198)
-0.369
(-0.508)
0.531**
R-squared
Cross section
Total Panel
(-1.947)
0.898
194
586
(3.119)
0.753
196
589
(-0.622)
0.889
76
255
(1.372)
0.748
77
257
(-1.487)
0.914
118
330
(3.612)
0.781
119
331
Outreach
Dummy of mature
institutions (>8 years)
Log of assets
Growth in GDP
Inflation
Human Development
Indicator
Portfolio at risk >30 days
Risk coverage
Write off ratio
operating expense ratio
Personnel allocation
ratio
Funding expense ratio
(VI)
Regulated institutions
0.000
(-0.370)
0.003***
(4.996)
0.000
(-0.910)
0.001*
(1.878)
-0.131*
(-1.851)
0.004
(1.483)
-0.000*
(-1.927)
0.006
(0.453)
0.004***
(2.742)
0.000
(-0.080)
0.030
(1.490)
-0.004
Sustainabi
lity
-0.079*
(-1.960)
0.010
(0.615)
-0.006
(-1.135)
-0.017
(-0.494)
6.860
(0.979)
-0.513***
(-4.116)
-0.001
(-0.439)
0.579
(0.607)
-0.680**
(-2.347)
-0.105
(-0.739)
-1.307**
(-2.073)
0.183
(V)
Other sample countries
institutions
Outreach
Sustainabili
ty
0.002***
0.073
(3.015)
(1.487)
0.006***
-0.014
(4.575)
(-0.298)
-0.000***
0.003
(-4.257)
(0.221)
0.000
0.004
(0.495)
(1.210)
-0.025
3.017*
(-0.846)
(1.673)
0.005**
-0.180
(2.254)
(-1.297)
-0.000*
0.001
(-1.926)
(0.887)
-0.009
-1.441***
(-1.287)
(-4.779)
-0.001
-0.316**
(-1.098)
(-2.526)
-0.004
0.017
(-1.405)
(0.211)
0.034
-0.940
(0.834)
(-0.882)
-0.006*
0.726***
0.002**
(2.077)
0.004***
(4.837)
0.000
(-0.974)
0.000
(3.073)
-0.071
(-2.191)
0.006
(3.179)
0.000
(-0.517)
-0.016***
(-3.771)
0.004**
(2.340)
-0.001
(-0.467)
0.045***
(3.330)
-0.001
Sustainabilit
y
0.048
(0.844)
0.010
(0.607)
-0.008
(-1.598)
0.003
(0.435)
0.128
(0.037)
-0.329***
(-5.592)
0.000
(0.049)
-0.688**
(-2.074)
-0.522***
(-4.294)
0.058
(1.069)
-0.512
(-0.503)
0.292
(-1.571)
0.779
95
277
(0.539)
0.679
95
277
(-1.838)
0.933
67
209
(-0.489)
0.885
134
412
(1.317)
0.780
136
415
(6.017)
0.766
99
312
Outreach
(VII)
Non-regulated
institutions
Outreach
Sustaina
bility
0.000
0.049
(0.269)
(0.313)
0.001**
-0.023
(2.088)
(-0.628)
0.000
0.003
(-0.915)
(0.632)
0.000
0.002
(-0.908)
(0.371)
0.037
2.001
(0.906)
(0.707)
0.001
-0.597**
(0.477)
(-2.443)
-0.000**
-0.005
(-2.616)
(-1.160)
-0.017
-1.058*
(-1.219)
(-1.857)
-0.027***
-1.847*
(-3.515)
(-1.910)
-0.006***
0.018
(-2.832)
(0.064)
0.032
-1.588
(1.009)
(-1.344)
-0.019***
1.452**
*
(-3.118)
(2.689)
0.937
0.742
55
55
156
157
Outreach is measured by number of active borrowers divided by total number of active borrowers group by each country and sustainability is measured by operational self-sufficiency. To check the robustness of our
results we also decompose the data according to different sub samples that are firstly, financial and non-financial institutions; secondly, Indian and other sample countries institutions and thirdly, regulated, nonregulated institutions and most of the time results demonstrate the consistent association in all of these specifications.
27
28