What Explains Microfinance Distribution Surplus? A Stakeholder-oriented Approach

M. Hudon and A. Périlleux

What are the drivers of productivity surplus distribution to microfinance stakeholders? This paper shows that the size of the institution is the main

indicator that can explain the gain in productivity surplus but also the surplus given to clients (decrease of interest rates) and staff. Moreover, cooperatives keep a significantly lesser part of their surplus for future growth, reserve, or

distribution to investors. Finally, larger, more subsidised MFIs, and particularly cooperatives, tend to give a greater part of their surplus to their employees.

Keywords: Microfinance, Surplus, Governance, Size, Subsidies, Cooperatives

JEL Classifications: O16, O50, G21

CEB Working Paper N° 10/045

November 2010

Université Libre de Bruxelles - Solvay Brussels School of Economics and Management

Centre Emile Bernheim ULB CP114/03 50, avenue F.D. Roosevelt 1050 Brussels BELGIUM

e-mail: ceb@admin.ulb.ac.be Tel. : +32 (0)2/650.48.64 Fax: +32 (0)2/650.41.88

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What Explains Microfinance Distribution Surplus?

A Stakeholder-oriented Approach

1 Marek Hudon

SBS-EM (Université Libre de Bruxelles), CERMi, Burgundy School of Business

2 Anaïs Périlleux

Faculté Warocqué (Université de Mons), CERMi, FNRS

Abstract

What are the drivers of productivity surplus distribution to microfinance

stakeholders? This paper shows that the size of the institution is the main indicator that can

explain the gain in productivity surplus but also the surplus given to clients (decrease of

interest rates) and staff. Moreover, cooperatives keep a significantly lesser part of their

surplus for future growth, reserve, or distribution to investors. Finally, larger, more

subsidised MFIs, and particularly cooperatives, tend to give a greater part of their surplus to

their employees.

Keywords – Microfinance, Surplus, Governance, Size, Subsidies, Cooperatives

JEL –codes: O16, O50, G21

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1. Introduction

Microfinance has historically been implemented around a double bottom-line, aiming

at both social and financial performances (Copestake et al., 2005). Nevertheless, the promise

that it would create a more inclusive financial sector, increasing efficiency and

professionalism in delivering financial services to poor clients (CGAP, 2004), has been

challenged by the lack of sustainability of many microfinance institutions (MFIs) while some

large MFIs are very profitable (Armendariz and Morduch, 2010).

Today, there is a clear trend to compare efficiencies of MFIs using indicators such as

staff productivity, operating expense ratio, or more sophisticated techniques such as data

envelopment or stochastic frontier analysis (Caudill et al., 2009; James et al., 2009; Hermes et

al., forthcoming). The methodology of the global productivity surplus (GPS) is both related

to the efficiency of MFIs and their social performance. It is associated with their productivity

since it analyses their capacity to generate a high combination of outputs on inputs. It can

also be related to the social performance of MFIs since the GPS methodology analyses the

distribution of this surplus between the various stakeholders while the definition of social

performance in microfinance is related to the relationship between the MFI and its

stakeholders (Copestake, 2007; Bédécarrats et al., 2009).

The surplus is part of the debate on the governance of MFIs. Corporate governance

aims indeed at encouraging the implication of economic agents in the productive process to

first generate some organizational surplus and then set up a fair distribution between the

partners (Maati, 1999 quoted in Labie, 2001). Since expectations of the various stakeholders

may differ, they can easily not all be fully satisfied (Mersland, 2009), those with the highest

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bargaining power becoming the most influential. In short, the surplus distribution gives

insight on what the main stakeholders receive when some productivity surpluses are realised.

The distribution of the surplus has been recently little studied except Périlleux, Bloy

and Hudon (2009); Honlonkou (2008) and Mbangala (2001). Honlonkou (2008) analyses the

surplus distribution of PADME, a MFI located in Benin. Using the comparison of averages

method, Périlleux et al. (2009) apply the productivity surplus theory to a large sample of MFIs

and find some differences between MFIs registered as cooperatives, non-profit organisations

(NPOs) and the more profit-minded shareholders firms (SHFs).

This paper broadens what was done by Périlleux et al. (2009) since it does not restrict

to the status of MFIs and tests a few potential indicators of the productivity surplus in

microfinance. It uses random effect panel estimations on a database of 761 observations,

providing 532 surpluses of 225 MFIs. It studies the main determinants of the surplus

distributed to three key stakeholders of MFIs: their clients, their staff and the “private value”:

the gross self-financing margin (GSFM) that accounts for the investors and reserve for future

investments. Our main result is that the size of the institution is the main indicator explaining

the surplus given to clients (decrease of interest rates). The ownership structure is however

relevant for the GSFM since cooperatives keep a significantly lesser part of their surplus for

future growth, reserve or distribution to investors. Finally, larger, more subsidised MFIs, and

particularly cooperatives tend to give a greater part of their surplus to their employees.

The rest of the article is structured as follows. The next section reviews the literature

on the performances indicators in microfinance and the potential role of surplus. Section 3

explains the application of the global productivity surplus theory to the microfinance sector,

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and its distribution among stakeholders. Section 4 presents the potential variables explaining

the surpluses. Section 5 details the methodology and the database. Section 6 discusses the

empirical results obtained. Finally, the last section draws some conclusions.

2. From financial and social performances towards efficiency and surplus

The double bottom line of social and financial performance is at the core of the

emergence of microfinance. While indicators of financial performance, such as financial self-

sufficiency or return on equity, have long been agreed upon, indicators of social performance

on the other hand are still often debated. As explained by Copestake (2007), social

performance can be defined in various ways. Most studies related to the social bottom line

of MFIs have used the average loan size divided by GDP per capita (for instance, Mersland

and Strøm, 2008), sometimes with the percentage of women (for instance, Hermes and

Lensink, forthcoming; Hermes et al., forthcoming) as a second indicator. The recent

guidelines for donors and investors, published by CGAP (2010), explain that it is difficult to

find consensual indicators of social performances that can easily be computed. It

recommends two indicators that can be used as minimum standards of social performance:

the number of clients and the average loan size as % of GNI per capita.

While the relevance of social indicators is regularly debated, the push for self-

sustainability has never been so high in the sector. Efficiency indicators have been introduced

in the public policy debate to help discriminate with greater accuracy than financial

performance alone between support-worthy and underperforming MFIs, regardless of the

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emphasis placed by each MFI on commercial success versus its impact on poverty

(Balkenhol, 2007). Many recent publications have used linear programming techniques such

as data envelopment analysis (DEA) (see for example Gutiérrez-Nieto et al., 2007). Other

recent publications apply stochastic frontier analysis (SFA), well known in the finance

literature, to microfinance. For instance, Hermes et al. (forthcoming) find that outreach and

efficiency of MFIs are negatively correlated. Caudill et al. (2009) use a mixture of cost

functions to determine whether MFIs active in Eastern Europe and Central Asia are

becoming more cost-effective over time. Their results indicate that about half of the MFIs in

the region are becoming more cost-effective over time and about half are showing no

improvement. Some of the determinants of efficiency can be influenced by MFI

management, like the choice of delivery technique, collateral requirements, or graduation

lending; others escape its control, and for these determinants (e.g., client density, scope of

clients’ viable income generating activities) managers cannot be held accountable (Balkenhol,

2007).

The high interest rates and the lucrative IPOs of Compartamos in 2007 have revived

an historic debate in microfinance related to the distribution of the revenues generated by the

MFIs between the various stakeholders (Ashta and Hudon, 2009). Périlleux et al. (2009) argue

that the global productivity surplus (GPS) theory could help to analyse the distribution of the

surplus generated by better productivity and therefore be also included as an indicator of

social performance. It could be related to the social responsibility of the MFIs vis-à-vis their

clients or staff, two elements of the analysis of the social performance management in

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microfinance (Bédécarrats et al., 2009). The next section presents the GPS methodology and

its application to microfinance.

3. The application of the GPS methodology to microfinance

We will use the GPS methodology to analyse surplus distribution in the microfinance sector.

According to the GPS, the productivity gain, which is the output quantities variations at

constant price minus the input variation at constant cost, is equal to the surplus distribution.

If we apply this equality to microfinance, we obtain:

=tGPS ∆OL t × it −1 - ∆OL t × prt −1[ ]− ∆DE t × it −1'' + ∆Dt × it −1

' + ∆N t × st −1[ ]= St1 + St

2 + St3 (1)

∆Output (O) ∆Input (I)

The first term is the productivity gain ( tGPS ), where the output variation (O) represents for

MFIs the outstanding loan portfolio variation tOL∆ at the previous year interest rate charged

to the clients ( 1−ti ). We must also take into account the bad debt (i.e., clients who have a

repayment delay) and therefore reduce the output. This is done by subtracting 1tOL −×∆ tpr

from O, where 1−tpr is the provision rate for clients who are suspected of repayment default.

The input ( I ) is composed of the suppliers of MFIs (the different parties bringing

some input): funds providers, working force providers (staff) and other providers. There are

two types of providers: savers and lending institutions (LIs). For savers, deposits expenses

are expressed as follows: ''1−×∆ tt iDE , the variation of the deposit amount at the previous year’s

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deposit interest rate ( ''1−ti ). For LIs, funding expenses are defined as follows: '

1−×∆ tt iD , the

variation of the funding amount at the previous year’s external lending interest rate ( '1−ti ).

Regarding working force providers, the expenses induced by employees can be noted as

follows:1−×∆ tt sN , the variation in the number of employees multiplied by the previous year’s

average salary. Finally, concerning other suppliers (the providers according to the accounting

definition), it is impossible to make a differentiation between price and quantity variations.

Due to this impossibility, these suppliers are not integrated in the calculation of surplus

formation but are only considered in terms of value variation in the surplus distribution

analysis.

The second term shows the allocation of the surpluses generated by productivity gains

between the different stakeholders of the MFI.

St1 is the surplus allocated to the clients (borrowers) of the MFI :

St1 = - ∆ it × (OL t -1 + ∆OLt ) - ∆prt × (OL t −1 + ∆OLt )[ ] (2)

This surplus is estimated by the interest rate variation multiplied by the portfolio. The

presence of a negative sign means that an interest rate decrease ( )0<∆i generates a gain for

the clients. This surplus must be corrected by the surplus gained or lost by bad

debts: ∆prt × (OL t −1 + ∆OLt ) , where pr ∆ represents the provision rate variation. The result

is that an increase of the provision rate generates a gain for borrowers, in the sense that they

have the potential to reimburse less.

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St2 is the surplus allocated to suppliers. In microfinance, there are four categories of

suppliers: the employees, the savers, the LIs, and the providers. Thus S2 can be

deconstructed in:

St2 = ∆st × (N t -1 + ∆N t ) + ∆i t

'' × (DE t -1 + ∆DEt ) + ∆i t -1' × (Dt −1 + ∆Dt ) + ∆(f t × Ft ) (3)

Employees Savers Lending institutions Providers

The surplus of employees or staff is related to the number of employees (N) and the

salary variation (∆s): a salary increase generates a surplus gain for the employees. The surplus

of savers is related to deposits (DE), the surplus of LIs to external funds (D), and both to

their respective interest rate variations. Thus, an increase in interest rate on savings (i’’)

and/or on external funding (i’) improves the savers’ and/or funding institutions’ positions.

The last category of suppliers is the providers. As explained, in this case, we cannot

make any distinction between price and quantity variations. Thus we take into account the

total variation in value of operating expenses: ∆f t × (Ft -1 + ∆Ft ) + ∆Ft × f t -1 = ∆(ft × Ft ).

Finally, there is the surplus part going to the MFI (St3), which partly represents the

shareholders’ surplus that accounts for the investors and reserve for future investments:

St3 = ∆GSFM t (4)

Thanks to this analysis, we can conclude that it is possible to identify the structure profile of

productivity gains (sources and uses) of each microfinance institution.

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The GPS methodology provides evidence on how surplus is shared between the

MFI’s stakeholders – crucial information that other methodologies cannot provide.

However, GPS offers no explanation on surplus performances, whether internal (for

instance, due to the mission of the institution), or external (for instance, due to the

environment or the donors). It only gives empirical evidence on the distribution of this

surplus.

In the rest of this paper, we make a distinction between the main stakeholders of the

MFI: the clients (borrowers), the staff, and the “private value” (GSFM); and the other

stakeholders: the savers, the LIs and the providers. Clients, staff, and the “private value” are

more important for our analysis because the MFI has more influence on the surplus allocated

to them, and its social mission is directly linked to the clients’ and the staff’s conditions.

Moreover, since not all institutions in our samples offer savings products, we do not consider

savers as key stakeholders.

4. Potential explanatory variables of the surpluses

Various factors can influence the distribution of the surplus generated by a MFI. Even

if we control for other elements, such as the age of the institution, the economy, or the

geographic location of the MFI, we particularly test the relevance of three indicators: the size

(number of clients), the level of subsidization, and the ownership structure (cooperative,

non-profit, or shareholder institution).

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Size of MFIs

Recent figures show an increase of efficiency for all categories of MFIs during the last

years (Caudill et al., 2009; Gutierrez-Nieto et al., 2007; James et al., 2009). Evidence has

suggested that the size of institutions matter both in terms of efficiency and financial or

social performances. Caudill et al. (2009) find that especially large MFIs are becoming more

efficient over time. Gutierrez-Nieto et al. (2007) find that NPOs are more cost-efficient in

issuing a large number of loans. Using a translog cost function with cost share equations,

James et al. (2009) also find some scale economies for MFIs in terms of efficiency. Low

financial performances of MFIs are often related to the public policy framework.

Cull et al. (2009) explain that the differences in terms of interest rates charged to the

borrowers are clearly related to the cost structure and that large institutions have lower

operating expense ratios. Nevertheless, above a certain threshold, lower operating expenses

are more a consequence of the size of the loan and of the number of services offered to the

clients. The 2009 MicroBanking Bullettin, which incorporates 1084 MFIs, mostly confirms

these results. In terms of financial performances, large MFIs have better financial self-

sufficiency, profit margin, or return on equity. Nevertheless, small, medium, and large MFIs

have very similar operational self-sufficiencies. Larger MFIs also have better efficiency ratios,

for instance in terms of staff productivity or personnel and operating expense ratios in this

database.

The figures from the 2009 MicroBanking Bulletin suggest that small and medium

MFIs tend to give smaller loans and serve more women. Nevertheless, the few results related

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to more refined indicators of social performance point to the importance of scale economies

in microfinance. The first results on a large database of MFIs suggest that large institutions

are also performing better in terms of social performance (Bédécarrats et al., 2009). More

precisely, MFIs with the largest loan portfolios score highest in the indicators “range and

quality of services” and “social responsibility” (including social responsibility vis-à-vis clients

and staff). Similarly, Copestake (2007) argues that economies of scale are important to

improve social performance. Based on these results, we can assume that surplus will follow a

trend similar to that of the social performance or financial expenses. Hence, larger

institutions will give a greater part of their surplus to their clients and employees, and keep

larger surplus for GSFM.

Hypothesis 1: Since larger MFIs benefit from more economies of scale, they should have

larger productivity surpluses (GPS) and thus be able to distribute more to their key

stakeholders. Hence, the size of the institution influences positively the surplus distribution

to their key stakeholders.

Subsidies

Even if most MFIs have started with donations or start-up in-kind assistance,

subsidies have always been debated in the sector. For instance, Armendariz and Morduch

(2010) warn that over-reliance on subsidies and poorly designed subsidies can limit scale and

undermine incentives to build strong institutions. Most donors agree that subsidies should

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primarily finance start-up expenses and products innovation. Moreover, CGAP considers

that subsidies should not be passed to clients since it would distort the market and refrain

new institutions from entering the microfinance sector (Helms, 2006). Subsidies should

therefore be invested to strengthen the institutions or even be given to shareholders to

attract new investors in the sector.

This policy orientation is also part of the notion of “smart subsidies” advocated by

Armendariz and Morduch (2010). Smart subsidies would aim at both maximising the social

benefits and minimising market distortion. Following the same rationale of market distortion,

we could assume that staff should not be better paid since it would also distort the labour

market in microfinance.

Balkenhol (2007) argues that donors should consider efficiency as the key indicator

guiding their decision. Aid agencies and governments should therefore help MFIs to improve

their efficiency rather than just focus on social and financial performances. Efficiency should

matter for donors since it can help them to discriminate between performing and under-

performing MFIs but also since their action would impact MFIs’ efficiency.

Even if microfinance subsidies have been largely discussed in the microfinance sector,

relatively few studies have provided empirical evidence on the impact of subsidies. Cull et al.

(2007) include subsidies in their study on the lending methodologies in microfinance (village-

bank, group-lending, and individual lending). Another exception is Hudon and Traça

(forthcoming), who analyze the impact of subsidies on staff productivity in a given sample of

MFIs. Their main result is that subsidies have helped MFIs to be more efficient until a

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threshold. Nawaz (2010) includes subsidies to analyze more sophisticated indicators of

efficiency and financial performance, and their potential impact in terms of mission drift.

Cull et al. (2009) show that the median NPOs turn to non-commercial funding and

donations with more frequency than the average MFIs. They find no clear evidence that

subsidization necessarily reduces the efficiency of MFIs. Their results also suggest more

heterogeneity of subsidies per borrower among NPOs while the median microfinance banks

received no subsidy. Finally, Hudon (forthcoming) finds some low relationship between the

level of subsidization of MFIs and the quality of their management.

Hypothesis 2: In line with donors’ consensus, subsidies should not decrease borrowers’

interest rates and thus not increase the clients’ surplus. They should however be used to

strengthen the MFIs or their attractiveness for investors and thus increase the GSFM

surplus. Finally, it should have a positive impact on the productivity of the MFI and thus

their global productivity surplus (GPS).

Governance

While governance of MFIs was not often studied in the past (Labie, 2001), many

recent publications include some governance indicators (Cull et al., 2009; Hartarska, 2005;

Mersland and Strøm, 2008; Mersland, 2009). In a study on contracts and governance in

microfinance, Mersland (2009) explains that for-profit and shareholders institutions are often

promoted because of their lower costs, governance designs, and openness to new investors,

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while NPOs would exhibit insufficient know-how and efficiency. Hansmann (2004) argues

that the intrinsic differences between SHFs, COOPs, and NPOs lie in the control of the

organization and those who receive the profit from it, an element that the surplus

distribution analyzes.

Mersland and Strøm (2008) argue that most practitioners have assumed that non-

profit institutions are more socially-oriented, as Rock et al. (1998) point out. Schreiner (2002)

suggests that more socially-oriented MFIs trade off narrow breadth, short length, and limited

scope with greater depth, while less socially-oriented MFIs trade off shallow depth with wide

breadth, long length; and ample scope.

The few empirical studies on governance in microfinance have provided contrasting

results. Mersland (2009) suggests that contracts are cheaper in COOPs and NPOs than in

SHFs, while the costs of ownership-practice are lower in SHFs. NGOs are however not

likely to reduce their cost over time, possibly because many NGOs cannot provide deposit

services (Caudill et al., 2009). According to Mersland and Strøm (2008), the differences

between microfinance NPOs and SHFs are minimal on outreach and sustainability, and the

SHFs’ superiority in scale and scope is not related to ownership type but rather to the legal

constraints on savings. In a study on microfinance corporate governance in Eastern Europe

and Central Asia, Hartarska (2005) also detects no effect of the type of institution.

Studies on the impact of ownership structures related to the social performance are

scarcer. The analysis by Bédécarrats et al. (2009) show that commercial banks score well on

product diversity and social responsibility to clients and employees, but do not serve the poor

or encourage client participation. Cull et al. (2009) show that NPOs charge higher interest

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rates than SHFs because of their costs and low loan size. The data of the recent publications

of MicroBanking Bulletin confirms the trend that NPOs charge higher interest rates.

Comparing average results of NPOs and SHFs, Périlleux et al. (2009) find that both NPOs

and SHFs tend to mainly keep their surplus within the MFI as a self-financing margin

(reserve accounts, future investments, and capital increase) rather than transferring it to their

clients (interest rates decrease) or their employees (salary increase). There is however a bigger

difference between these two types of institutions and the cooperatives since cooperatives

tend to give the largest part of their surplus to the employees and providers.

Hypothesis 3: If NPOs are more socially-oriented, they would decrease client’s interest rates

and thus increase their surplus while SHFs would favor GSFM. Moreover, cooperatives will

have different distribution policies since they are members-owned institutions. More

specifically, they should also favor clients’ surplus.

5. Methodology and data

In order to test the three hypotheses defined in the previous section, we specify a

panel data model, where the dependant variable is successively the global productivity surpus

and then the pourcentage of the surplus allocated first to the clients (the borrowers), the

staff, and the “private value”, and finally to the savers, the LIs and the suppliers. The

explained variables of interest are the size of the MFI (related to Hypothesis 1), the subsidy

intensity of the MFI (Hypothesis 2) and the ownership status (Hypothesis 3). We also control

for a set of variables: the age of the MFI, its geographic provenance, the size of the loan (as

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proxy for the poverty of the clientele), and the macro-economic performance of the country

of activity.

We estimate the parameters of the model using a robust clustering method to correct

for cross-sectional heteroskedasticity and serial correlation. It is thus a within-panel

correlation across multiple observations from a same MFI. We opt for a random effects

model. One of the main advantages of the random effects method is its ability to estimate

time-invariant variables (Hausman and Taylor, 1981). Our model contains strategic dummy

variables such as the ownership structure, as this option better fit with the analysis’

objectives. The random effects model is often used to conduct analyses on MFIs’ behaviors

and performances (Lensink and Mersland, 2009; Hartarska, 2005; Vanroose and D’Espallier,

2009).

The random effects model reduces the risk of bias resulting from potential omitted

variables since it takes into account the unobservable institution-specific residual variation in

the surplus allocation process. Moreover, there is no risk of endogeneity-bias resulting from

reverse causality in the model, because explained variables are calculated at the beginning of

the surplus allocation process. Hence, surplus variations resulting from the allocation process

could not influence the value of the regressors at the beginning of the period. The general

specification of the model is presented as follows:

tiititit

itiitiitti

uYEARGNIALS

AGEGEOSUBGOVSIZESplVar

,876

543211,

++×+×+×+×+×+×+×+×+=+

µββββββββα

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Where SplVar is the surplus variation respectively for the three main stakeholders,

the GPS, and the two other stakeholders. SIZE is the natural logarithm of the total number

of borrowers served by the MFI. This variable indicates the differences generated by the size

(such as economies of scale). GOV is a set of status dummies that shows organizational

structure differences between NPOs, COOPs, and SHFs. SUB is the total amount of

subsidies received by the MFI divided by its outstanding loans portfolio. GEO is a set of

regional dummies that capture the regional differences. AGE is divided in three categories:

young, intermediate, and old. Table 1 illustrates the definition of each category, according to

the MicroBanking Bulletin. ALS is the natural logarithm of the average loan size of the MFI

divided by the GNI per capita in the MFI hosted country. GNI is the natural logarithm of

the GNI per capita which captures the standard of living of the MFI’s country. YEAR is a set

of dummies, which control for year-specific effects (e.g., changes in economic conditions). µ

is the institution-specific effect that captures all unobservable institution-specific variations,

and u is the ramdom error term.

< Insert Table 1 >

Database construction

We construct the dataset with data gathered by two leading microfinance rating

agencies1: Microfinanza and PlaNet Rating. Our database includes information from balance

sheets and income statements, in addition to other variables such as the number of

borrowers and employees, and indicators of operational and financial sustainability. Our

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database is a Panel with observations going from 1999 to 2008. On average, we have 3.4

years of observations per MFI.

The financial statements we use constitute one of the most trustworthy source of

information, since MFIs in our sample have all been audited during the rating process

(contrary to voluntarily released data provided in other databases). These MFIs are amongst

the largest and best-managed institutions in the world. Therefore, given the well-established

concentration of microfinance clients (Honohan, 2004), our sample should be representative

of the universe of microfinance activities. As a matter of facts, basic statistics obtained from

our sample appear to be similar to those coming out the largest databases in microfinance.

For instance the 1,084 MFIs in the 19th MicroBanking Bulletin [MBB] (MicroBanking

Bulletin, 2009) yield an average Operational Sustainability of 111% compared to ours of

115%. The average number of borrowers is 9,013 for the MBB compared to 9,960 in our

database; the average nominal yield of is 31% in the MBB and 36% in our database; and,

finally, the average staff productivity is 103 in the MBB while it is 123 borrowers per staff in

our database.

We use the 761 observations of the 225 MFIs to calculate the surpluses that are

diffetrentials between two years of observations. This finally gives 532 surpluses. Among

these 532 observations of our sample, 266 are NPOs, 169 are SHFs, and 97 are COOPs.

Geographically, out of the 532 observations, 139 are related to MFIs from Sub-saharian

Africa (SSA), 188 from Latin America (LA), 81 from Eastern Europe (EastEur), 46 from

Noth-Africa and middel East (NA-ME), and 78 from Asia. Only 131 out of the 532 observed

MFIs offer savings.

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Descriptive statistics

Table 2 shows the summary statistics of our sample. This latter is dominated by non-

profit and Latin American institutions. Regarding the “Size”, on average MFI serve 9,960

borrowers with an average loan size of 790 US$. The stock of subsidies received by MFIs in

the past represents 1.36 of their outstanding portfolio. The “Age” shows that a majority of

MFIs are older than 4 years (73%) and 35% older than 8 years. Finally, the GNI per capita of

the countries where MFIs are located reaches on average 1,619 US$ per inhabitant.

Regarding the descriptive statistics of surplus variables, the providers and the « private

value » are the two stakeholders which, on average, beneficiate from a gain in the surplus

distribution process. The staff also has, on average, a positive but relatively low surplus

(0.01). Conversely, the LIs and the clients (borrowers) and savers register a negative surplus

mean. Finally, the GPS is negative on average, which means that MFIs tend to register a gain

in productivity. These results are in line with Périlleux et al. (2009). Indeed, a negative sign of

the GPS indicates that in the surplus distribution process, the GPS brings value to the other

stakeholders thanks to a productivity gain.

< Insert Table 2 >

6. Estimation and results

We start the empirical analysis by looking at the correlation matrix between the

continuous explanatory variables (Table 3). We conduct this analysis in order to examine the

21

multicollinearity dimension. The two-sided Pearson correlation method enables to point out

the level of significance of these correlations. The table stresses that variables are generally

correlated with each other, except for subsidies. However, the correlation coefficients remain

relatively low. They do not exceed 0.8, the level at which collinearity problems appear

(Kennedy, 2008).

< Insert Table 3 >

After looking at the matrix correlation, we conduct multivariate analyzes. For each

type of dependant variable, we calculate two regressions: firstly, without the SUB variable,

ignoring the external aid. Secondly, we include the SUB variable to see what it captures.

Multivariate analyses will be done in two steps. Firstly, we analyze the drivers of the the

global productivity surplus. Table 4 shows that the size of the MFI significantly affects the

surplus gained thanks to a higher productivity (negative sign of the GPS). This means that

bigger MFIs can generate economies of scale and obtain higher gains in productivity. As it

can be seen in column 4.a, as well as in 4.b, the other parameters do not influence

productivity significantly. Interestingly, neither status nor subsidies affect the productivity of

the MFI.

<Insert Table 4>

Secondly, we study the variation of the surplus allocation for the three types of

stakeholders of the MFI who are the most relevant in terms of policy orientation for all

MFIs. Table 5 contains respectively the panels for clients, staff, and GSFM surplus variation.

We respectively discuss the results for these three types of stakeholders.

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<Insert Table 5>

The Panel 1 investigates the variables influencing the clients’ surplus variations,

without (column 1.a) and with (column 1.b) subsidies. The column 1.a shows that the size

positively and significantly (10%) influences the client surplus variation. This means that

bigger MFIs tend to allocate a higher surplus to their clients. The age also has a significant

impact, especially during the first years: INTER has a larger and more significant coefficient

than OLD. This means that, compared to younger MFIs, MFIs that are older than 4 years

will allocate a greater part of their surplus to their clients, but on a decreasing trend. Column

1.b shows exactly similar results and comes up with a significant, positive coefficient of

subsidies on the clients surplus variation. Finally, it is interesting to mention that there is no

significant difference depending on the ownership structure.

Panel 2 explores the variables influencing the staff surplus variation. Column 2.a

shows that MFIs under a cooperative status allocate a significantly greater part of their

surplus to their employees than MFIs under a NPO or SHF status, the COOP coefficient

being positive and significant (at 1%) for both regressions, where we control for subsidies or

not. The size of the MFI also has a positive influence on the surplus allocated to employees:

bigger MFIs tend to allocate a greater part of their surplus to their employees. The column

2.b shows that subsidies also have a significant (1%), positive influence on staff surplus.

Greater amounts of subsidies enable MFIs to better consider their employees. Finally, the

INTER coefficient is negative and significant (10%): older MFIs tend to allocate a lesser part

of their surplus to their employees. However, this influence does not persist in the long term,

the OLD variable not being significant.

23

Panel 3 analyzes variables explaining the surplus allocated to the GSFM that accounts

for the investors and reserve for future investments. Column 3.a shows that the cooperative

status has a significant (at 5%), negative influence on the surplus allocated to the GSFM.

Thus, cooperatives tend to keep a lesser part of their surplus as “private value”. These

organizations are less preoccupied by growth and, as members are also owners, they do not

have private shareholders who could make pressure for higher profits. The size of the MFI

positively influences the surplus retained as GSFM: bigger MFIs are more concerned by

growth or by the remuneration of their shareholders. Moreover, size seems to have a bigger

effect on GSFM than on surplus to staff as suggested by the coefficients (0.068 vs 0.031).

Column 3.b supports that subsidies increase the surplus allocated to the GSFM. We finally

compute a third regression including the square of subsidies to address potential marginal

effects. Column 3.c shows that the marginal effect of subsidies decreases at higher level of

subsidy intensity as suggested by the negative coefficient of the square of the subsidy

indicator2.

Thirdly, we look at the results for the panel analysis for the last three stakeholders

(Table 6). We also conduct for each of them a regression with and without the SUB variable,

respectively summarized in columns a and b.

< Insert Table 6 >

Panel 4 shows that neither size, nor subsidies, nor ownership influence the surplus to

savers. Actually, no indicator significantly influences this surplus. Nevertheless, the results for

24

the drivers of this surplus may be biased due to the fact that many MFIs in the sample are

not allowed to take deposits.

Regarding the LIs, Panel 5 shows that the size and the status of the MFI do not

influence this surplus. However, column 5.b stresses that subsidies have a negative influence

on their surplus variation. Indeed, the subsidies decrease the lending interest rate and MFIs

end up paying lower rates for their loans. Also, the age of the MFI affects the surplus

allocated to the LIs: older MFI can get loans from these external financial institutions at a

better price

Finally, concerning the providers, we have stressed that this surplus has to be

interpreted with great caution, due to the impossibility to make a distinction between

quantity and price variations. Panel 6 indicates that the only significant variable is the

subsidies, which negatively affect the surplus allocated to the providers.

To sum it all up, our empirical results confirm the first hypothesis for the key

stakeholders (i.e., the clients, staff and the “private value”): size had a positive influence on

the surplus allocation to them. This seems to be related to economies of scale, which increase

the gain in productivity. Indeed, size also has a significant, negative influence on the GPS,

and a decrease in the surplus going to the GPS is tantamount to an increase in productivity.

However, the size of the MFI does not influence the surplus allocated to the outsiders of

MFIs (LIs and providers).

Our results do not fully verify the second hypothesis since subsidized institutions

distribute a larger part of their surplus to the clients. Subsidised MFIs also allocate a larger

25

part of their surplus to staff. Nevertheless, subsidies have a more significant effect on the

GSFM (the “private value”), even if the marginal effect decreases. Finally, subsidies

negatively affect the surplus allocated to other stakeholders (LIs and suppliers).

Finally, our empirical results mainly contradict the third hypothesis since there is no

significant difference between NPOs and SHFs for all stakeholders. Nevertheless,

cooperatives distribute their surplus differently since they allocate a significantly greater part

of their surplus to their employees and a lesser part to the GSFM.

7. Conclusion

While financial indicators have been in place for a few years, efficiency and social

performances are still much discussed in the microfinance sectors. The global productivity

surplus is a productivity indicator, rarely used in microfinance, which analyzes the creation

and distribution of the productivity surplus generated from one year to the other between

some main stakeholders of MFIs. In this paper, we analyze the drivers of the surplus

distributed to various stakeholders of MFIs. We also analyze variables influencing the

productivity gain of these institutions.

We find that the size of the institutions is a main element to explain the increase of

MFI productivity (according to the results from the GPS panel). This productivity gain seems

to benefit three key stakeholders: clients, staff, and the “private value”, whereas it has a

negative impact on the rates paid to the lenders, and no impact on providers.

26

Subsidies also have a significant, positive impact on the surplus allocated to these

three key stakeholders, but in a higher proportion for the GSFM. Interestingly, subsidies do

not influence the productivity of the MFI. Also, they do not benefit outsiders, as they

influence negatively, at a significant level, the surplus allocated to lenders and providers.

The ownership structure is only relevant in the case of COOPs since they keep a

significantly lesser part of their surplus for future growth, reserve, or distribution to

investors, and allocate a significantly greater part of their surpluses to their staff. No

significant difference appears, in term of surplus allocation policies, between NPOs and

SHFs. These results are in line with Périlleux et al. (2009) who explain that the main

difference in surplus distribution between the various ownership structures is not between

NPOs and SHFs, but rather with cooperatives. Moreover, they are also in line with Mersland

and Strøm (2008) who find little differences between NPOs and SHFs in terms of social and

financial performances.

As for clients, our results show that institutions decreasing their interest rates are

mainly larger but also older and relatively more subsidized. These results are in line with

surveys on other social indicators, such as Copestake (2007) or Bédécarrats et al. (2009), who

also highlight that the size is an important driver of social performance.

These results may give new food for thought for policy makers or more generally on

the evaluation of MFIs. While many suggested that size and scale economies could be

instrumental to be more efficient or profitable, it could also be useful to decrease interest

rates and give better salaries, which could be linked to the social responsibility of MFIs.

Further research, with a larger database including more years could therefore give additional

27

information, for instance on the volatility of the GPS. Moreover, the GPS could also be

compared to other indicators of social performances and financial performances.

28

LIST OF TABLES

Table 1: Age categories; The Microbanking Bulletin Methodology (2009) Age Number of year

Young MFI’s age<5 years

Inter 4 years <MFI’s age<9years

Old 8 years<MFI’s age

Table 2: Summary of variables

Variable Description Obs Mean Std. Dev. Min Max

MFI size

Borrowers Number of borrowers 526 9960 15512 74 110266

MFI Subsidies

SUB Subsidies/ Oustanding Loan 531 1.36 17.29 0 398

MFI governance

NP Non-profit 532 0.5 0.5 0 1

COOP Cooperative 532 0.18 0.39 0 1

SHF Shareholder Firm 532 0.32 0.47 0 1

MFI Age

YOUNG Young 532 0.27 0.45 0 1

INTER Intermediate 532 0.38 0.48 0 1

OLD Old 532 0.35 0.48 0 1

MFI geography

ASIA Asia 532 0.15 0.35 0 1

LA Latin America 532 0.35 0.48 0 1

AFSS Africa: Sub-Saharian 532 0.26 0.44 0 1

EASTEUR Eastern Europe 532 0.15 0.36 0 1

NAME North Africa and Middle-East 532 0.09 0.28 0 1

Others

Avloan Loan Portfolio/Borrowers 524 790.06 1437.9 10.18 14896.8 GNI per capita

Gross National Investment per capita 531 1619 1469 90 9610

Surplus

GPS Global Productivity Surplus 532 -0.12 1.91 -1.22 32.31

SPClient Surplus to Clients 532 -0.06 0.74 -6.97 1

GSFM Gross Self-Financing Margin 532 0.14 0.52 -3.30 1

SPSav Surplus to Savers 532 -0.002 0.097 -1.55 0.52

SPStaff Surplus to Staff 532 0.01 0.45 -4.93 0.93

SPLI Surplus to Lending Institutions 532 -0.12 1.58 -32.94 0.82

SPProv Surplus to Providers 532 0.15 0.33 -2.5 1

29

Table 3: Correlation coefficients among the explanatory variables

SIZE SUB OLD INTER GNI ALS

SIZE 1

SUB 0.009 1

OLD 0.203*** 0.056 1

INTER 0.051 -0.033 -0.569*** 1

GNI -0.228*** -0.056 -0.103** 0.086** 1

ALS -0.265*** -0.037 0.055 -0.031 -0.425*** 1

Level of Significativity : *** if P-value=<0.01, ** if P-value=<0.05, * if P-value=<0.10

Table 4: Surplus allocation of the global productivity surplus (GPS)

Global productivity surplus variation

a b

SIZE : LnBorr -0.184*** -0.186***

[0.05] [0.05]

SUB : sub/port 0.002 [0.00] GOV : COOP -0.204 -0.201

[0.19] [0.19]

GOV : SHF -0.071 -0.072

[0.21] [0.21] AGE : OLD 0.283 0.282

[0.19] [0.19]

AGE : INTER 0.346 0.354

[0.26] [0.26]

LnGNI -0.058 -0.052

[0.18] [0.18]

LnALS -0.088 -0.086

[0.08] [0.08]

GEO : LA 0.117 0.121

[0.31] [0.31]

GEO : AFSS 0.465 0.469

[0.33] [0.33]

GEO : ASIA 0.616 0.631

[0.51] [0.51]

GEO : NAME -0.094 -0.089

[0.28] [0.28] Model Stat N Wald chi2 R2 – within R2 - between

523

96.11*** 0.019 0.051

522

524.66 0.019 0.053

Level of Significativity : *** if P-value=<0.01, ** if P-value=<0.05, * if P-value=<0.10

30

Table 5: Surplus allocation of the three key stakeholders

1. Surplus to clients 2. Surplus to Staff 3. Surplus to GSFM

1.a 1.b 2.a 2.b 3.a 3.b 3.c

SIZE : LnBorr 0.055** 0.056** 0.031** 0.031** 0.068*** 0.069*** 0.074***

[0.02] [0.02] [0.02] [0.02] [0.02] [0.02] [0.02]

SUB : sub/port 0.001*** 0.001*** 0.002*** 0.053**

[0.0004] [0.00] [0.0003] [0.02]

SUB^2 -0.0001**

[0.0005]

GOV : COOP -0.041 -0.046 0.134*** 0.133*** -0.118** -0.126** -0.107*

[0.10] [0.11] [0.05] [0.05] [0.06] [0.06] [0.06]

GOV : SHF 0.017 0.017 -0.012 -0.012 -0.030 -0.030 -0.028

[0.08] [0.08] [0.04] [0.04] [0.06] [0.06] [0.05]

AGE : OLD 0.167* 0.163* -0.064 -0.066 -0.110 -0.117 -0.118*

[0.09] [0.09] [0.07] [0.07] [0.07] [0.069] [0.07]

AGE : INTER 0.232** 0.230** -0.101* -0.101* -0.056 -0.057 -0.058

[0.09] [0.09] [0.05] [0.05] [0.06] [0.06] [0.06]

LnGNI -0.051 -0.05 -0.024 -0.023 -0.008 -0.006 0.011

[0.05] [0.05] [0.03] [0.03] [0.04] [0.04] [0.04]

LnALS 0.03 0.033 0.021 0.022 0.040 0.044 0.060**

[0.04] [0.04] [0.02] [0.02] [0.03] [0.03] [0.03]

GEO : LA 0.032 0.035 -0.026 -0.024 -0.085 -0.079 -0.055

[0.12] [0.12] [0.08] [0.08] [0.08] [0.08] [0.08]

GEO : AFSS -0.147 -0.149 -0.154* -0.154* -0.161 -0.165 -0.170

[0.13] [0.13] [0.08] [0.08] [0.12] [0.12] [0.11]

GEO : ASIA -0.209 -0.209 -0.006 -0.004 0.048 0.051 0.067

[0.15] [0.15] [0.08] [0.08] [0.09] [0.09] [0.09]

GEO : NAME -0.026 -0.025 -0.016 -0.015 0.140 0.141 0.135

[0.14] [0.14] [0.08] [0.08] [0.11] [0.11] [0.10]

Model Stat

N Wald chi2 R2 – within R2 - between

523 302.58***

0.014 0.0784

522 415.69***

0.014 0.081

523 31.94** 0.0055 0.0806

522 61.23*** 0.0055 0.0828

522 134.99*** 0.0253 0.1195

522 226.53*** 0.0246 0.133

522 1.44e+06***

0.016 0.159

Level of Significativity : *** if P-value=<0.01, ** if P-value=<0.05, * if P-value=<0.10

31

Table 6: Surplus allocation of the other stakeholders

4. Surplus to savers 5. Surplus to lending

institution 6. Surplus to providers

4.a 4.b 5.a 5.b 6.a 6.b

SIZE : LnBorr -0.002 -0.002 0.029 0.031 0.008 0.007

[0.00] [0.00] [0.03] [0.03] [0.01] [0.01]

SUB : sub/port -.00002 -0.002*** -0.001*** [0.00] [0.00] [0.00] GOV : COOP -0.014 -0.014 0.171 0.170 0.017 0.021

[0.02] [0.02] [0.11] [0.11] [0.04] [0.04]

GOV : SHF -0.003 -0.003 0.030 0.032 0.000 0.000

[0.01] [0.01] [0.18] [0.18] [0.03] [0.03] AGE : OLD 0.014 0.014 -0.227** -0.224** -0.015 -0.011

[0.01] [0.01] [0.10] [0.10] [0.04] [0.04]

AGE : INTER 0.013 0.013 -0.356* -0.364* -0.033 -0.032

[0.01] [0.01] [0.21] [0.21] [0.03] [0.03]

LnGNI -0.002 -0.002 0.134 0.126 -0.009 -0.011

[0.01] [0.01] [0.16] [0.16] [0.03] [0.03]

LnALS 0.007 0.007 -0.048 -0.052 0.018 0.016

[0.01] [0.01] [0.05] [0.05] [0.01] [0.01]

GEO : LA -0.001 -0.001 -0.072 -0.078 -0.036 -0.039

[0.01] [0.01] [0.23] [0.23] [0.04] [0.04]

GEO : AFSS 0.012 0.012 0.005 0.002 -0.066 -0.064

[0.02] [0.02] [0.22] [0.22] [0.08] [0.08]

GEO : ASIA -0.011 -0.011 -0.477 -0.495 -0.021 -0.022

[0.02] [0.02] [0.44] [0.44] [0.05] [0.05]

GEO : NAME 0.009 0.009 -0.034 -0.040 -0.062 -0.062

[0.02] [0.02] [0.19] [0.19] [0.06] [0.06] Model Stat N Wald chi2 R2 – within R2 - between

523 14.32 0.043 0.030

522 14.38 0.043 0.030

523 775.68***

0.036 0.030

522 955.51***

0.036 0.036

523 467.51 0.078 0.036

522 582.70***

0.078 0.048

32

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Endnotes

1Other articles using databases from rating agencies are, for instance, Mersland and Strøm (2008) or Hudon and Traça (forthcoming). 2We have also computed regressions including the square of the subsidy indicators for the other surpluses. As none of these regressions report a significant coefficient of the square of subsidies, these are not reported.