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

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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

1The 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.

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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

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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 self-

sustaining 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.

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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

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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.

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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.

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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 M ijt+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.

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.

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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

financial sustainability is measured by return on assets (ROA) and operational self-

sufficiency (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, ε ijt 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.

9 NPIs include NGOs, credit unions and other institutions and the category of PIs include banks, rural banks and NBFIs.

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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

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∈GDP= logGDP t−logGDP t−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

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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.

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]

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.11The primary data is retrieved from MIX market during November/December 2012 from http://www.mixmarket.org/.12See http://data.worldbank.org/indicator

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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%

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.

13 For brevity we are not including the descriptive statistics of sub-sample data but it can be available on request. 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.

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[INSERT TABLE 4 ABOUT HERE]

4.1. Outreach

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.

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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

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

15

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

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

16

(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 non-

financial institutions, Indian and other sample countries institutions, regulated and non-

regulated 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

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

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

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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Table 1: Variables descriptionsName Calculated as Explanation Data source

Dependant variables

Outreach

Number of active borrowers  Log values of total borrowers and clients of MFIs

The number of credit clients at the end of each period

MIX market

Average Loan Balance per Borrower GNI (%)

Normalized by gross national income Outreach indicator

Number of active borrowers divided by total borrowers in each country

Normalized by dividing total borrowers per country

Outreach indicator

Percentage of Female Borrowers (%)

Number of female borrowers divided by total clients

Outreach indicator

Sustainability

Return on assets (%) (Net operating income minus taxes)/ divided by average assets

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.

Operational self-sufficiency (%)  Financial revenue divided by (financial expense + impairment loss + operating expense)

It measures how well MFI can cover its operating cost through its operating revenues.

Institution control

age  Year of experience as an MFI Dyoung (<8 years old) and dmature (>8years old) dummies are created for age and put dyoung dummy as base dummy.

size The natural logarithm of total assets Proxy of size measureInstitution’s type  Profitable and non-profitable

institutionsDifferent ownership structure of MFIs

Regulatory status  Regulated and non-regulated institutions

Different regulatory structure of MFIs

World Bank Development Indicatorscountry control

Growth in GDP  Size of the economy of the country in current US dollars in billions.

Proxy to measure the country size

HDI Composite country index covering education, life expectancy and income.

Proxy of quality of life

Inflation Inflation, consumer prices (annual %)

Risk Ratios

Portfolio at risk >30 days (%) Portfolio at Risk > 30 days divided by gross loan portfolio

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.

MIX market

Risk coverage ratio (%) Impairment loss allowance divided by PAR > 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

Write-off ratio (%) Write offs divided by average gross loan portfolio

Value of loan written off divided by average gross loan portfolio

Cost Ratios Operating expense ratio (%) Operating expense divided by average gross loan portfolio

Cost ratio

22

Personnel Allocation Ratio (%) Loan officers divided by personnel Cost ratio

Profitability RatiosFunding expense ratio (%) Financial expense divided by average

assetsProfitability ratio

Capital to assets ratio (%) Total Equity divided by Total Assets Profitability ratio

Table 2: Descriptive statistics of VariablesOutreach SustainabilityAll data (N = 576) All data (N = 589) Mean Maximum Minimum Std. Dev. Mean Maximum Minimum Std. Dev.

Average loan balance per borrower / GNI 0.216 6.357 0.009 0.329

Number of active borrowers 315817 6430000 565 935737

Percentage of female borrowers 0.862 1.041 0.026 0.241Number of active borrowers / assets 0.007 0.073 0.000 0.005AGE 13.036 51.000 1.000 8.755 13.005 51.000 1.000 8.698

Dummy of young institutions 0.288 1.000 0.000 0.453 0.287 1.000 0.000 0.453

Number of active borrowers / total borrowers in each country 0.005 0.061 0.000 0.009Return on assets 0.005 0.308 -0.972 0.076Operational self-sufficiency 1.124 3.357 0.179 0.304

Dummy of mature institutions 0.712 1.000 0.000 0.453 0.713 1.000 0.000 0.453Assets 51664338 1410000000 132970 145000000 51041214 1410000000 132970 143000000Growth in GDP 6.757 9.801 1.596 2.283 6.710 9.801 1.596 2.295

Human development indicator 0.513 0.686 0.424 0.053 0.513 0.686 0.424 0.054Inflation 9.254 22.564 3.418 3.466 9.336 22.564 3.418 3.541Portfolio at risk > 30 days 0.065 0.994 0.000 0.129 0.068 1.000 0.000 0.135Risk coverage ratio 2.799 80.507 0.000 7.575 2.759 80.507 0.000 7.498Write off ratio 0.010 0.247 -0.001 0.027 0.010 0.247 -0.001 0.027Operating expense ratio 0.166 1.871 0.009 0.158 0.166 1.871 0.009 0.158Personnel allocation ratio 0.595 1.225 0.014 0.186 0.593 1.225 0.014 0.188Funding expense ratio 0.063 0.286 0.000 0.031 0.063 0.286 0.000 0.031

23

Capital to asset ratio 0.178 0.985 -0.482 0.174 0.179 0.985 -0.482 0.176Table 3: Correlation

DYOUNG

DMATURE PFB NAB

NAB/AST

NABDTNAB

ALBPBG ROA OSS AGE AST

GGDP HDI INFL

PAR30 RSKC WOR OER PAR FER

CAR

Dummy of young institutions 1Dummy of mature institutions -1.000 1Percentage of female borrowers -0.034 0.034 1Number of active borrowers -0.136 0.136 0.063 1Number of active borrowers / assets 0.013 -0.013 0.283

0.051 1

Number of active borrowers / total borrowers in each country -0.122 0.122 -0.131

0.641 -0.127 1

Average loan balance per borrower / GNI -0.131 0.131 -0.260

-0.05

6 -0.262 -0.010 1

Return on assets -0.179 0.179 0.2270.14

6 0.105 0.099 0.019 1Operational self-sufficiency -0.177 0.177 0.201

0.232 0.090 0.159 0.051

0.766 1

AGE -0.648 0.648 0.0500.28

2 0.081 0.108 0.0300.17

2 0.153 1

Assets -0.140 0.140 0.0250.94

6 -0.032 0.666 -0.0410.12

0 0.183 0.240 1

Growth in GDP 0.104 -0.104 0.1360.02

8 0.163 -0.195 -0.1510.04

4 0.059-

0.115 0.037 1

Human developmnet indicator 0.196 -0.196 -0.108

-0.04

0 -0.033 0.049 -0.243

-0.01

0-

0.023-

0.154 0.011 0.281 1

Inflation 0.098 -0.098 -0.151

-0.05

5 -0.183 0.098 0.003

-0.05

8-

0.080-

0.069 -0.025-

0.197 0.168 1

Portfolio at risk > 30 days -0.139 0.139 -0.065

0.007 -0.078 0.014 0.019

-0.11

8-

0.179 0.141 0.029 0.005 0.041 0.053 1

Risk coverage ratio -0.009 0.009 0.085

-0.03

9 0.047 -0.017 -0.0070.08

3 0.067-

0.044 -0.044-

0.095-

0.067-

0.013 -0.151 1

Write off ratio 0.078 -0.078 -0.2680.01

9 -0.118 0.030 -0.047-

0.37-

0.293-

0.074 0.032-

0.082 0.041 0.180 0.261-

0.09 1

24

8 0

Operating expense ratio 0.313 -0.313 -0.260

-0.10

8 -0.100 -0.109 -0.100

-0.71

5-

0.509-

0.214 -0.106-

0.106-

0.011 0.123 0.012

-0.04

40.39

0 1

Personnel allocation ratio 0.086 -0.086 0.236

0.065 0.143 -0.042 -0.202

0.015 0.011

-0.180 0.045 0.139

-0.132

-0.210 -0.126

0.042

-0.03

4

-0.04

6 1

Funding expense ratio 0.046 -0.046 0.170

0.043 0.009 0.030 -0.079

0.044

-0.015

-0.179 0.091 0.246 0.249 0.164 -0.037

0.103

-0.07

4

-0.17

4

-0.00

9 1

Capital to asset ratio 0.161 -0.161 -0.2380.11

8 0.006 0.005 -0.0720.09

1 0.114-

0.008 0.071 0.012 0.100 0.035 0.003

-0.01

60.16

30.24

5

-0.05

2

-0.31

2 1

Table 4: Outreach

Benchmark model

Risk ratios Cost ratios Profitability ratios All dataWith

selected ratios

Dummy of mature institutions (>8 years)

0.001 0.001 0.000 0.000 0.000 0.000 0.001 0.001 0.001 0.001 0.001 0.001(0.677) (0.591) (-0.277) (0.422) (-0.350) (0.365) (1.177) (0.909) (0.539) (0.645) (0.560) (1.601)

Log of assets 0.001*** 0.002*** 0.002*** 0.002*** 0.002*** 0.002*** 0.002*** 0.003*** 0.002*** 0.001*** 0.002***0.004**

*(3.238) (3.600) (4.082) (3.698) (4.194) (5.343) (3.466) (6.912) (3.282) (3.249) (3.278) (7.198)

Growth in GDP0.000 -0.000* -0.000* 0.000 -0.000** 0.000 -0.000** -0.000* 0.000 0.000 0.000 0.000*

(-1.119) (-2.007) (-1.840) (-0.828) (-2.250) (-1.036) (-2.126) (-1.646) (-0.629) (-0.918) (-0.612) (-1.651)

Inflation0.000* 0.000 0.000* 0.000* 0.000* 0.000** 0.000** 0.000** 0.000** 0.000** 0.000** 0.000(1.983) (1.481) (1.768) (1.994) (1.699) (2.307) (2.075) (2.132) (2.314) (2.011) (2.321) (1.41)

Human Development Indicator 0.055*** 0.042*** 0.024 0.045*** 0.025 0.029** 0.020 -0.015 0.034** 0.052*** 0.034**

-0.064**

*(3.962) (2.842) (0.925) (3.131) (0.928) (2.119) (0.621) (-0.558) (2.357) (3.926) (2.375) (-2.941)

Portfolio at risk >30 days 0.002* 0.003*0.003**

*

(1.908) (1.940) (4.105)

Risk coverage0.000 0.000 0.000*

(-1.592) (-1.236) (-1.764)

Write off ratio -0.002 -0.001

-0.008**

*

(-0.242) (-0.247) (-2.832)

25

operating expense ratio0.001** 0.002*** 0.003**

(2.598) (5.003) (2.081)

Personnel allocation ratio

-0.003* -0.003 -0.002

(-1.831) (-1.582) (-1.178)

Funding expense ratio0.018* 0.018 0.039**(1.695) (1.591) (2.802)

Capital to assets ratio-0.001 0.000 -0.004*

(-0.487) (-0.115) (-1.947)

R-squared 0.88 0.90 0.88 0.88 0.88 0.89 0.90 0.90 0.89 0.88 0.89 0.898Cross sections 318 301 257 287 243 300 236 226 297 318 297 194Observations 1159 1006 823 907 737 1003 809 729 985 1156 985 586

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.

Table 5: Financial sustainability

Benchmark model

Risk ratios Cost ratios Profitability ratios All dataWith

selected ratios

Dummy of mature institutions (>8 years)

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) (0.707) (1.175) (1.007) (1.363) (1.205) (-0.964) (-0.684) (1.083) (0.779) (0.959) (0.647)

Log of assets 0.051** 0.041** 0.070*** 0.051** 0.064** 0.033 0.119*** 0.097*** 0.055** 0.053** 0.056** 0.012(2.324) (2.013) (3.001) (2.271) (2.401) (1.554) (9.082) (5.051) (2.734) (2.296) (2.642) (0.915)

Growth in GDP -0.006 -0.005 -0.008* -0.009 -0.007 -0.006 -0.005 -0.005* -0.010 -0.006 -0.010 -0.004(-1.118) (-0.996) (-1.627) (-1.603) (-1.210) (-1.268) (-1.557) (-1.634) (-1.535) (-1.013) (-1.465) (-1.026)

Inflation -0.001 0.001 0.000 0.000 0.000 -0.002 -0.001 -0.001 -0.002 -0.001 -0.002 0.001(-0.382) (0.230) (-0.029) (0.039) (-0.023) (-0.588) (-0.260) (-0.221) (-0.426) (-0.349) (-0.417) (0.158)

Human Development Indicator

-1.570 -0.898 -2.258 -1.693 -1.782 -1.515 -3.679** -3.391* -1.069 -1.630 -1.120 0.535

(-1.247) (-0.705) (-1.241) (-1.143) (-0.803) (-1.181) (-2.368) (-1.996) (-0.765) (-1.333) (-0.832) (0.216)

Portfolio at risk >30 days -0.391*** -0.464***

-0.436**

*

(-5.969) (-6.173) (-4.962)Risk coverage -0.002 -0.004 -0.001

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(-0.822) (-1.464) (-0.831)

Write off ratio-

1.339*** -1.077*** -0.851**

(-3.378) (-2.833) (-2.544)

operating expense ratio -0.257*** -0.125**

-0.555**

*

(-5.446) (-2.678) (-3.759)

Personnel allocation ratio

0.087 0.068 0.020

(0.977) (0.769) (0.198)

Funding expense ratio

-1.227** -1.184** -0.369(-2.756) (-2.462) (-0.508)

Capital to assets ratio

0.083 0.073 0.531**

(0.664) (0.529) (3.119)

R-squared 0.73 0.74 0.80 0.73 0.82 0.74 0.71 0.72 0.73 0.73 0.73 0.753Cross-sections 318 301 258 290 246 304 234 228 301 318 301 198Observations 1168 1007 822 922 742 1028 780 730 1009 1168 1009 589

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.

Table 6: Sub sample regression results(I) (II) (III) (IV) (V) (VI) (VII)

All data Financial institutions Non-financial institutions Indian institutions Other sample countries institutions

Regulated institutions Non-regulated institutions

Outreach Sustainability

Outreach Sustainability

Outreach Sustainability

Outreach Sustainability

Outreach Sustainability

Outreach Sustainability

Outreach Sustainability

Dummy of mature institutions (>8 years)

0.001 0.025 0.001** 0.042 0.002* 0.052 0.000 -0.079* 0.002*** 0.073 0.002** 0.048 0.000 0.049(1.601) (0.647) (2.538) (0.759) (1.693) (1.088) (-0.370) (-1.960) (3.015) (1.487) (2.077) (0.844) (0.269) (0.313)

Log of assets 0.004*** 0.012 0.004*** 0.063 0.003*** -0.003 0.003*** 0.010 0.006*** -0.014 0.004*** 0.010 0.001** -0.023(7.198) (0.915) (4.030) (1.514) (6.663) (-0.089) (4.996) (0.615) (4.575) (-0.298) (4.837) (0.607) (2.088) (-0.628)

Growth in GDP 0.000* -0.004 0.000 -0.003 0.000* -0.007 0.000 -0.006 -0.000*** 0.003 0.000 -0.008 0.000 0.003(-1.651) (-1.026) (-0.805) (-0.942) (-1.818) (-0.924) (-0.910) (-1.135) (-4.257) (0.221) (-0.974) (-1.598) (-0.915) (0.632)

Inflation 0.000 0.001 0.000* 0.004 0.000 -0.004 0.001* -0.017 0.000 0.004 0.000 0.003 0.000 0.002(1.41) (0.158) (1.715) (0.589) (0.262) (-0.549) (1.878) (-0.494) (0.495) (1.210) (3.073) (0.435) (-0.908) (0.371)

Human Development Indicator

-0.064*** 0.535 -0.052 -0.906 -0.056*** 0.377 -0.131* 6.860 -0.025 3.017* -0.071 0.128 0.037 2.001(-2.941) (0.216) (-1.532) (-0.229) (-3.982) (0.152) (-1.851) (0.979) (-0.846) (1.673) (-2.191) (0.037) (0.906) (0.707)

Portfolio at risk >30 days 0.003*** -0.436*** 0.006* -0.326*** 0.001 -0.579*** 0.004 -0.513*** 0.005** -0.180 0.006 -0.329*** 0.001 -0.597**

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(4.105) (-4.962) (2.590) (-5.181) (1.289) (-2.763) (1.483) (-4.116) (2.254) (-1.297) (3.179) (-5.592) (0.477) (-2.443)Risk coverage 0.000* -0.001 0.000 0.001 0.000 -0.001 -0.000* -0.001 -0.000* 0.001 0.000 0.000 -0.000** -0.005

(-1.764) (-0.831) (0.089) (0.748) (-1.640) (-0.700) (-1.927) (-0.439) (-1.926) (0.887) (-0.517) (0.049) (-2.616) (-1.160)Write off ratio -0.008*** -0.851** -0.021* -0.067 -0.005 -0.897** 0.006 0.579 -0.009 -1.441*** -0.016*** -0.688** -0.017 -1.058*

(-2.832) (-2.544) (-1.863) (-0.153) (-0.965) (-2.072) (0.453) (0.607) (-1.287) (-4.779) (-3.771) (-2.074) (-1.219) (-1.857)operating expense ratio 0.003** -0.555*** 0.007*** -0.183 0.000 -0.955*** 0.004*** -0.680** -0.001 -0.316** 0.004** -0.522*** -0.027*** -1.847*

(2.081) (-3.759) (2.797) (-1.327) (-0.275) (-4.187) (2.742) (-2.347) (-1.098) (-2.526) (2.340) (-4.294) (-3.515) (-1.910)Personnel allocation ratio

-0.002 0.020 -0.001 0.160** -0.002* -0.078 0.000 -0.105 -0.004 0.017 -0.001 0.058 -0.006*** 0.018(-1.178) (0.198) (-0.676) (2.481) (-1.717) (-0.435) (-0.080) (-0.739) (-1.405) (0.211) (-0.467) (1.069) (-2.832) (0.064)

Funding expense ratio 0.039** -0.369 0.060*** -0.842 0.006 -0.369 0.030 -1.307** 0.034 -0.940 0.045*** -0.512 0.032 -1.588(2.802) (-0.508) (3.469) (-0.619) (0.555) (-0.343) (1.490) (-2.073) (0.834) (-0.882) (3.330) (-0.503) (1.009) (-1.344)

Capital to assets ratio -0.004* 0.531** -0.002 0.247 -0.004 0.879*** -0.004 0.183 -0.006* 0.726*** -0.001 0.292 -0.019*** 1.452***

(-1.947) (3.119) (-0.622) (1.372) (-1.487) (3.612) (-1.571) (0.539) (-1.838) (6.017) (-0.489) (1.317) (-3.118) (2.689)R-squared 0.898 0.753 0.889 0.748 0.914 0.781 0.779 0.679 0.933 0.766 0.885 0.780 0.937 0.742Cross section 194 196 76 77 118 119 95 95 67 99 134 136 55 55Total Panel 586 589 255 257 330 331 277 277 209 312 412 415 156 157

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, non-

regulated institutions and most of the time results demonstrate the consistent association in all of these specifications.

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