Efficiency of Microfinance: Do Subsidies Matter? · 2020. 1. 21. · Caudill et al. (2009), Cull et al. (2009) and Hudon and Traca (forthcoming) incorporate measures of subsidies

Efficiency of Microfinance: Do Subsidies Matter?

Abstract:

Application of Data Envelopment Analysis (DEA) to the microfinance institutions is a recent

phenomenon. While no publications include the role of subsidies in their analysis of microfinance

efficiency, this paper measures the impact of various types of subsidies on the efficiency of

microfinance institutions (MFIs). Our results suggest that while subsidies globally decrease

efficiency of MFIs, subsidized borrowings improve the efficiency of MFIs as assessed using the

non‐parametric DEA. Particularly larger institutions are more efficient. Nevertheless, no effect of

the percentage of women, the clientele targeted (outreach) or a specific ownership structure is

found.

Key Words: Microfinance, Subsidies, Efficiency, Non‐parametric analysis

JEL Codes: G21, H2, H21, C14

1. Introduction

Efficiency of the financial institutions is crucial in competitive markets. While efficiency of

conventional financial institutions has often been studied, analyses of the efficiency of

microfinance institutions (MFIs) are less frequent due to the late emergence of this sector.

Nevertheless, many MFIs are still dependent on subsidies, what has brought the debate on their

efficiency under the spotlight. In traditional Banking Literature, the evaluation of financial

performance by using non parametric efficiency techniques i.e. Data Envelopment Analysis (DEA),

is a very common practice (Athanassopoulos (1997); Seiford and Zhu (1999); Camanho and Dyson

(2005)). However, its application to the microfinance institutions is more recent phenomenon.

Exceptions using DEA approach in microfinance are Gutiérrez‐Nieto et al. (2007) and (2009)

analyzing the relationship between social and financial efficiency and Biener and Eling (2010) who

focus on the performance of microinsurance programmes. None of three publications include the

role of subsidies in their analysis of microfinance efficiency.

However the efficiency analysis of MFIs based on conventional production and

intermediation model approach in non‐parametric efficiency analysis framework is hard to justify

because of their reliance on subsidies. The overall equation linking capital and labor inputs into

profits and social change still proves difficult to master without accommodating the subsidized

inputs (Cull et al. 2007). Therefore, measuring their efficiency demands the role of subsidies to be

accounted for, an area, largely neglected in the efficiency and productivity analysis of microfinance

institutions. Moreover, some donors and practitioners are concerned that excessive subsidization

will hamper the promise of sustainability of MFIs but also undercut both scale and efficiency

within the MFI, and possibly distort the market by favouring more inefficient institutions

(Armendariz and Morduch, 2010; Hudon and Traca, forthcoming) . To date only a few studies have

been done which have taken into account the role of subsidies into the assessment of financial

performance or efficiency of MFIs by employing parametric techniques (Cull et al., 2007 ; Hudon &

Traca, forthcoming; Hudon, forthcoming), let alone non‐parametric efficiency analysis. One

exception is Caudill et al. (2009) who use a transcendental logarithmic form to estimate efficiency

on a sample of institutions from Eastern Europe and Central Asia.

This paper fills this gap by incorporating the role of subsidy dependence in the DEA analysis

of MFIs. This quality financial information has been generated directly from the audit reports1 of

the 179 MFIs for two years (2005 and 2006). As a starting point, this essay calculates total

subsidies aggregating the revenue grants received by the MFIs and two opportunity costs: the

concessionary borrowings and the subsidized equity. With subsidy data at our disposal, this study

aims to resolve this key issue. Do these subsidies improve the performance of MFIs by enhancing

their efficiency? To that extent, this study aims to investigate in particular, some specific

hypothesis related to the efficiency of microfinance by employing with and without subsidy

analysis. Another important relationship to be estimated is between the efficiency of MFIs with

their financial sustainability and subsidization.We will also test if outreach, the ownership

structure, the size and the percentage of women of the MFIs have an impact on the global

efficiency.

Our results do not show some positive trends of subsidy dependence on the efficiency of

MFIs. The DEA efficiency scores show only a marginal positive impact of total subsidies on the

financial efficiency of MFIs when they are included as output. On the one hand, Subsidy

dependency clearly decreases efficiency of MFIs. Subsidizing credit lines would however have

better impact in terms of efficiency rather than revenue grants or subsidized equity. On the other

hand, particularly larger institutions are more efficient. No effect of the percentage of women, the

clientele targeted (outreach) or a specific ownership structure is found.

1 The audit reports have been taken from the Mix Market Website (hhp://www.mixmarket.org). The MIX MARKET is a global, web‐based microfinance information platform. It provides information to sector actors and the public at large on Microfinance Institutions (MFIs) worldwide, public and private funds that invest in microfinance, MFI networks, raters/external evaluators, advisory firms, and governmental and regulatory agencies

The paper is organized as follows. In the next section, to start off, we review the literature

on efficiency and public policy in microfinance. In Section 3, we describe the theoretical

background of non parametric efficiency analysis and the subsidy dependence index that we use in

the paper. We then present in Section 4 the database and some basic descriptive statistic. Section

5 provides efficiency results with and without total subsidies as output of the efficiency indicator.

Section 6 highlights the empirical evidence by employing the regression analysis. Finally, a

conclusion is given at the end.

2. Efficiency and Public Policy in Microfinance

Like the conventional financial institutions, the efficiency and productivity of MFIs has

generally been measured by conventional financial ratios or indicators such as staff productivity or

operating expense ratio (Balkenhol, 2007). An example is Hudon and Traca (2009) who use staff

productivity. Most new studies on cost efficiency use more sophisticated indicators of efficiency

such as data envelopment analysis (DEA) or stochastic frontier analysis (SFA) to calculate this

frontier. Gutiérrez‐Nieto et al. (2007) use DEA to a sample of 30 Latin American MFIs to test

twenty‐one specifications. They apply principal component analysis to explain efficiency scores by

means of four principal components. Their results show different rankings using DEA and more

conventional benchmarks and financial indicators.

Hermes et al., (forthcoming) estimate efficiency of 435 MFIs with stochastic frontier

analysis and find that outreach and efficiency of MFIs are indeed negatively correlated, what

suggests some trade‐off between these dimensions. Focusing on the country‐level financial

environment and using the same dataset and methodology, Hermes et al. (2009) show that

financial development and MFIs efficiency are positively correlated. Using data from Eastern

Europe and Central Asia, Caudill et al. (2009) estimate the cost function for MFIs using the

transcendental logarithmic form for all estimations. Their results show that larger MFIs offering

deposits operate more cost effectively over time.

Most microfinance programs have started with the support of donors since the emergence

of the sector during the 70s. After many years of operations, many of these MFIs still rely on

donors’ funds to finance their growth or even cover their operations. Hence a deeper

understanding of the true costs associated with subsidization of microfinance to the society, and

its impact on the efficiency of MFIs is needed. Balkenhol (2007) argues that efficiency should be

regarded as a key indicator by donors who not only focus on social and financial performances.

Although public policy has been largely debated in the microfinance sector, relatively few

studies have provided empirical evidences on the impact of subsidies. Morduch (1999) report the

difficulties to get reliable data on subsidies and all sort of donations. Subsidies traditionally come

from direct grants or donations, that are often reported but may also indirect with in‐kind asset or

training facilities. A large part of them are in fact granted through subsidized credit lines or soft

loans much cheaper than the market rates. These adjustments can make a big difference. For

instance, Morduch (1999) calculated that the sum of the direct and indirect subsidies to the

Grameen between 1985 and 1996 reached $144 million while the Grameen reported $ 1.5million.

As suggested by Hudon and Traca (forthcoming), theoretical arguments on the efficiency

effects of subsidies go in both directions. On the one hand, effects of soft‐budget and the

traditional moral hazard argument, related to the information problems of donors, suggest that

high level of subsidization would decrease the incentive to be efficient. An example of this is Bhutt

and Tang (2001) who argue that subsidies to microfinance NGOs end up funding inefficient and lax

management practices resulting in limited outreach and high loan default. On the other hand,

subsidies granted for infrastructure or human resources can help MFIs to increase their efficiency.

Many institutions that are now profitable and efficient have been subsidized in the past.

Caudill et al. (2009), Cull et al. (2009) and Hudon and Traca (forthcoming) incorporate

measures of subsidies in their analysis of efficiency of MFIs. Cull et al. (2009) analyze standard

efficiency indicators from the Microbanking Bulletin and suggest that subsidy does not necessarily

reduces the efficiency of MFIs but the nongovernmental microfinance organizations do tend to

have higher operating costs. Hudon (forthcoming) find that the level of subsidies granted per year

is related to the management quality but not the subsidies divided by the gross loan portfolio or

the total equity. Caudill et al. (2009) find that MFIs receiving lower subsidies operate more cost

effectively over time. Hudon and Traca (forthcoming) find subsidies have helped MFIs to be more

efficient until a threshold. Nevertheless, Caudill et al. (2009), Cull et al. (2009) and Hudon and

Traca (forthcoming) measure of efficiency is based on the stock of subsidies received in the past,

donated equity. In the next section, we will describe the Data Envelopment Analysis (DEA)

approach and the subsidy indicators.

3. Description of efficiency and subsidy indicators

3.1. Efficiency: DEA Model and Input Output Variable

For the efficiency analysis of the microfinance institutions, a two‐stage analysis has been carried

out. Data Envelopment Analysis (DEA) approach is used to estimate technical and pure efficiency

scores of the MFIs. The advantages of using the DEA technique to gauge efficiency are well

documented in the literature. DEA framework can handle multiple outputs and inputs. Thus, in the

context of MFIs efficiency analysis, it can incorporate both the outputs of outreach and

sustainability along with other inputs into a single framework. Neither has it required any price

information for the dual cost function nor parametric functional form for the production function.

Table 1 depicts the summary of inputs and outputs selected for this study. The main

objective of estimating a production function is to explain the quantity of output produced given

certain levels of inputs and other relevant factors that might explain the quantity of output

produced. In traditional financial literature two models i.e. Production Model and Intermediation

Model are popular depending upon what one thinks an institution do. The majority of the studies

in banking efficiency literature are based on the input oriented constant returns to scale CCR

model (Charnes et al, 1978). In the production model approach, financial institutions are treated

as firms that use physical input, employees and expend money in order to obtain deposits, grant

loans and collect revenues. We assume the output oriented Production model with variable

returns to scale is better suited to microfinance institutions rather than constant returns to scale

model. MFIs are indeed interested in increasing outreach i.e. lending loans to poor people which

commensurate with not only their social mission but also contributes towards sustainability as

well by collecting more revenues from lending. In addition to that they compete in an imperfect

economic environment as the markets for MFIs are not as well developed as the conventional

banking sector. And they always have restricted amount of money and human resource (Inputs) to

spend on unlike commercial banks which can generate money from shareholders. In the context of

output oriented model, this essay asks a specific question “By how much the output quantities are

proportionally expanded without altering the input quantities used?”

The selection of specifications with correct inputs and outputs in the context of MFIs is very

important. Based on the literature, we have selected a few inputs and outputs. This study uses LR‐

ACE2 as a general specification where gross loan portfolio and financial revenues are taken as an

output and assets, operating costs and number of staff as an input. In addition to that, we have

also used specifications L‐ACE and R‐ACE, where the former put emphasis on granting loan as main

objective of MFIs and latter signifies revenue collection as main objective of MFIs. The other

specifications used are basically the different combination of treating subsidies as an input and

output with the above general specifications.

2 The left part in all the specifications show outputs and the right part depict inputs.

Table 1

Inputs and Outputs in Efficiency Specifications

Variable Variable name Not. Definition Unit Papers using this input/ output

Input Total assets A Total of all net asset accounts ($)

Berger and Humphrey (1997),

Seiford and Zhu (1999) and Luo

(2003).

Input Operating Cost C

Expenses related to operations,

such as all personnel expenses,

rent and utilities, transportation,

office supplies, and depreciation

($)

Athanassopoulos (1997), Berger

and Humphrey (1997) and Pastor

(1999).

Input Number of

Staff E

The number of individuals who are

actively employed by the MFI. No.

Athanassopoulos (1997), Berger

and Humphrey (1997), Sherman

and Gold (1985), Seiford and Zhu

(1999) and Luo (2003

Output Gross loan

portfolio L

Outstanding principal balance of

all of the MFI’s outstanding loans ($)

Sherman and Gold, 1985;

Athanassopoulos, 1997; Berger

and Humphrey, 1997; Wheelock

and Wilson, (1999).

Output Financial

revenue R

Revenue generated from the gross

loan portfolio and from

investments plus other operating

revenue

($)

Pastor (1999) and Seiford and Zhu

(1999)

Output Financial

Revenue‐

Subsidy

Rs Financial revenues without Total

subsidies (R‐S) ($)

On top of the traditional variables used in DEA specification, we also include a new output variable

incorporating subsidies. To this end, we remove the total subsidies received by the MFI from its

financial revenues. This is done to adjust the financial revues of the MFI, part out its outputs, as if

they would not have received any subsidy.

Subsidies into DEA Framework

Many indicators of subsidies are used in the literature. An historical indicator is the subsidy

dependence index (SDI). The SDI is ratio of subsidy received by a MFI to revenue from loans to the

target group; it indicates whether a MFI could compensate society for the opportunity cost of

public funds used in a short time frame and still show a profit (Schreiner and Yaron, 2002). Other

indicators used in the literature include donated equity (Caudill et al., 2009), donated equity

divided by total equity (Hudon and Traca, forthcoming), donated equity divided by the age or

outstanding loan of the MFI (Hudon, forthcoming; Hudon and Traca, forthcoming) or subsidized

borrowing (Caudill et al., 2009). Similarly to Caudill et al. (2009), we use a mix of subsidy

indicators.

The Formula for our Subsidy Indicator (SUBLOAN) is:

( )[ ]LPS

LPSubBor

LPSubEq

LPRGcmAmERGSAdjustedTotalSUBLOAN

LPortfoliodingLoansPOuts=++=−×+×+==

tanubsidies

(1)

Where:

LP = Average annual outstanding loan portfolio of the MFI

RG = Revenue Grant

E = average annual equity;

m = Market Interest rate: Interest rate the MFI is assumed to pay for borrowed funds if access to

concessional borrowed funds (A) were eliminated.

A = Average annual outstanding concessionary‐borrowed funds (Average public debt)

c = Interest rate paid on concessionary borrowed funds (Public debt)

Hence, our measure of total subsidies (S) is composed of two parts. The first one are the direct

grants or money transfers (RG) coming from the income statement and which are also used in

Caudill’s (2009) direct grant. Donated equity, used by Hudon and Traca (forthcoming) and Hudon

(forthcoming) is the stock of grants received in the past.

The second part is related to opportunity costs. These are costs that MFIs do not have to

pay, either through subsidised equity (SubEq) or concessionary borrowings (SubBor). Similarly to

Morduch (1999) and Schreiner and Yaron (2002), subsidised equity is calculated by multiplying the

average equity in the year by the commercial market interest rate. Concessionary borrowings are

calculated by multiplying the total borrowings of the MFI by the difference between the market

interest rate and what the MFI actually pays. These three components are part of Yaron (1992)

subsidy dependence index (SDI) who also adds the total profit or loss of the MFI.

4. Database and descriptive statistics

Our data come from the Audit Reports of MFIs taken from Microfinance Information

eXange Inc website. Through this information exchange platform, individual MFI can provide

financial and outreach data and the Mix Market ranks these data for quality using a 5‐star system,

where 5 is the most complete data available, while 1 is the least complete data available (usually

the number of borrowers and some other outreach indicators but little financial information). Only

5‐star MFIs, which include MFIs reporting audited financial statements and a Rating on their online

profile, have been incorporated. 179 MFIs in 54 countries have been chosen based on the clarity

of their respective Audit Reports in general and subsidy figures in particular. The most important

variable to extract from the audit reports for subsidy calculations is the public debt/concessional

borrowing. Therefore MFIs have been selected in large part on the quality and clarity of public

debt figures in their respective audit reports.

Our sample for this essay consists of total 340 observations (170 for the each year 2005

and 2006) which is assumed to be representative of the whole microfinance sector. For example,

the 1,084 MFIs in the 19th MicroBanking Bulletin [MBB] (MicroBanking Bulletin, 2009) yield an

average Operational Sustainability of 111% compared to ours of 124.4%. The average nominal

yield is 31% in the MBB and 30.3% in our database and, finally, the average staff productivity is

103 in the MBB while it is 143 borrowers per staff in our database. As most samples using

databases from rating agencies or Mix Market, our sample is probably biased versus the largest

and better managed around the world. Given the well‐established concentration of microfinance

clients in the largest institutions (Honohan, 2004), the sample is however representative of the

universe of microfinance activity. 46% MFIs of our sample are registered as NGO, followed by the

NBFIs (29%). MFIs with “Bank” status constitute only 16% of total sample. Almost half of the MFIs

(48%) offer both group and individual lending services followed by MFIs which lend exclusively to

the individuals (32%). Geographically one‐third of MFIs locate in Latin America (33%) and almost

one‐fourth in Africa (23%). South Asian MFIs constitute only about 14% of the total MFIs in the

sample.

INSERT TABLE 2 (Appendix)

Table 2 presents a summary statistics of the variables used as an inputs and outputs in the

DEA framework along with other social and organizational variables used in the regression

framework. The statistics for total subsidy and its components (subsidized equity, subsidized

borrowings and revenue grants) are also given.

Table 3 presents the efficiency analysis of the MFIs bifurcated into various categories. The

results for the technical efficiency can been calculated using constant returns to scale efficiency

(crs), variable return to scale efficiency (vrs) and scale efficiency (Scale). However in the Table 3,

the average efficiency scores for specification LR‐ACE have been presented employing only much

realistic output oriented model with variable returns to scale. Which shows that on average CA

&EE and Latin American MFIs are the efficient ones while ME & NA and South Asians are the worst

ones relatively. MFIs with cooperative and NGO status are more efficient than others while MFIs

with individual and village lending methodology are on average more efficient than others.

Moreover MFIs with no saving designs; those which are not regulated and those which do not

provide other services are on average more efficient than their counterparts.


The correlation matrix in Table 4 reveals important relationships among the input‐output

variables used in the efficiency specifications; those used as categorical variables and other

organizational variables. The table stresses that variables are generally correlated between them,

except for subsidies. However, the correlation coefficients remain relatively low. They do not

exceed 0.8, the level at which collinearity problem appears (Kennedy, 2008).


5. Efficiency With and Without Subsidy Analysis

We first compare the average values of the technical efficiency results of with and without

subsidy specifications. The specifications entertained are LR‐ACE vs. LRs‐ACE (without subsidies)

and R‐ACE vs. Rs‐ACE (without subsidies). Since less output is generated without subsidies, the

efficiency will decrease. What is of interest is the magnitude of the decrease.

Comparing the general efficiency specification LR‐ACE (with subsidies) vs. LRs‐ACE (without

subsidies) in Table 5; we can see that efficiency decreases in the three cases when subsidies are

removed. Averages of CRSTE, VRSTE and SE have decreased from 0.582, 0.688, and 0.831 to 0.528,

0.634 and 0.806 respectively. The average efficiency scores can also be interpreted in another way.

For example, scores of 0.582 and 0.688 show that average output of MFIs can be increased by

41.8% and 31.2% with the same use of inputs assuming constant and variable returns to scale

respectively. Considering specification R‐ACE (where MFIs sole objective is to increase revenues),

the decrease in efficiency (from 0.480, 0.566 and 0.840 to 0.266, 0.288 and 0.753 for CRSTE, VRSTE

and SE respectively) is more resounding when subsidies have been subtracted from the revenues

in specification Rs‐ACE (without subsidy). For specification LR‐ACE, 13 and 20 MFIs have become

fully efficient with the injection of subsidies by employing CRSTE and VRSTE respectively. While 22

MFIs become fully scale efficient with the subsidies. On the other hand 11 and 16 MFIs previously

fully efficient become less efficient with subsidies for CRSTE and VRSTE respectively. Further, 10,

32 and 20 MFIs remain fully efficient irrespective of the subsidies for CRSTE, VRSTE and SE

respectively.

Table 5 also shows the results, in terms of the z‐value and significance levels, of the

Wilcoxon signed rank test of the differences between with and without‐subsidy efficiency scores.

The comparison between with and without‐subsidy efficiency scores for specifications LR‐ACE and

R‐ACE indicate that the efficiency of MFIs are significantly reduced (as reflect the positive sign)

without subsidies for all CRSTE, VRSTE and SE.


To sum up, the above efficiency analysis show that subsidies enable to increase efficiencies

significantly when incorporated into the non‐parametric DEA framework. Nevertheless, if we want

to analyze whether subsidies can make a significant difference towards efficiencies, we will

employ a parametric analysis and including some control variables. The following section will

investigate this issue in parametric framework using the Tobit regression analysis.

6. Tobit Regression Approach

6.1. Methodology

Tobit Regression analysis are carried out to test a series of hypotheses concerning the

relationship between financial efficiency and other indicators related to MFIs productivity,

organizational, outreach, sustainability and social impact amid subsidies. The model is censored if

one can at least observe the exogenous variables while it is truncated if the observations outside a

specified range are totally lost” (Amemiya, 1984:3). In this case, a Tobit censored regression model

is appropriate3 because it can accommodate the censored DEA efficiency score since the values of

the dependent variable lie between 0 and 1 with some values achieving the highest value of 1. This

study has taken the output oriented technical efficiency with variable returns to scale as

dependent variable for Tobit regression analysis using the panel data.

The Equation is as follows

+ C +LogWomen + LogAge + hLogOutreac + LogBor + OSS + SUB + SUB + = LogEff

iii7i6

i5i4i32i2i1i

εμβββββββα

(2)

Where iLogEff is the logarithm of the efficiency indicator; iSUB is the subsidy variables,

composed of SUBLOAN (total subsidies divided by loan portfolio), RG (revenue grants by loan

portfolio), SUBBOR (subsidised borrowings divided by loan portfolio) and SUBEQ (subsidised equity

divided by loan portfolio). Similarly to Hudon and Traca (forthcoming) we include the square of the

subsidy variable 2iSUB is the square term of the subsidy variables. iOSS is the operational self‐

sufficiency, iLogBor is the logarithm of the number of borrowers; hLogOutreac is the logarithm of

3 For literature see for example Chakraborty et al., 2001 ; McCarty and Yaisawarng, 1993; Gilen and lall, 1997 and Chilingerian, 1995 among others

our outreach indicator (loan size divided by GNI per capita in purchasing power parity);

LogAgei is the logarithm of the number of years since the MFI has been operating and

iLogWomen is the logarithm of the percentage of female borrowers, for MFIi.

Ci are the controls for Region, Status, Lending Methodology, Saving, Regulated and Other services

i.e. health, education etc in addition to providing financial services or not. Geographic region in

which the MFI operates are classified into 6 regions: Africa (A); East Asia and the Pacific (EA&P);

Eastern Europe and Central Asia (EE&CA); Middle East and North Africa (MENA); Latin America and

the Caribbean (LAC); South Asia (SA). Lending methodology is classified into 4 categories:

Individual (I); Individual & Solidarity/Group (IS); Group/Solidarity (S); Village banking (V). MFIs are

classified into 5 categories of ownership structure: Nongovernmental organizations (NGO); Bank

(B); Non‐banking financial intermediaries (NBFI); Rural Bank (RB); Cooperatives (Coop.).

The omitted variable categories are: for region, Africa; for status, Non Banking Financial

Institution (NBFI); for lending methodology, Group lending; and others are MFIs with no saving

feature, not regulated and no other services.

The base regression above describes the determinants of efficiency with particular

emphasis on the impact of subsidies on the efficiency. The SDI square term is included in the

regression to investigate the potentially marginal impact of subsidization on the efficiency as

suggested by Hudon and Traca (forthcoming). A random‐effect Tobit model has been employed to

analyze the panel data. It should be noted that Honore (1992) has developed a semi parametric

estimator for fixed‐effect Tobit models but there does not exist a sufficient statistic allowing the

fixed effects to be conditioned out of the likelihood. Therefore, unconditional fixed‐effects

estimates are biased (Stata 9 manual).

6.2. Results

Equations (1) ‐ (8) in Table 6 present the overall regression equations with base efficiency

specification LR‐ACE as dependent variable. The sample size consists of 165 MFIs out of 179 MFIs

for which the subsidy figures are available for both years. 14 MFIs have been dropped due to the

unavailability of women borrower information and negative values of subsidized borrowing

variable. Regression 1 includes the quadratic form of composite subsidy indicator (SUBLOAN)

along with all the control variables. The relationship between subsidy dependence and efficiency

does not hold as evident from the insignificant coefficient of the linear term of SUBLOAN.

However, the quadratic term is positive and significant which shows that after the threshold level,

more subsidization start contributing towards efficiency. Nevertheless, only one MFI, FACECAM in

our sample is above this threshold for year 2005. The linear coefficient of subsidy explains

variation in efficiency only once we drop the highly significant borrower variable in regression 2,

where the sign of the linear term becomes negative and significant.


The coefficients of the size (borrowers) in all the equations are also found to be significant

and positive even after dropping the age and OSS variables in Regression 3 & 4 (with quadratic and

with out quadratic term respectively) thus depicting that with increase in clients, MFIs tend to be

more efficient. This is in line with Caudill et al. (2009) who also find that especially large MFIs are

becoming more efficient over time. The age of the institution and the Operational Self Sufficiency

do not explain any variation in efficiency as evident from their insignificant coefficients in all the

regression. The total subsidy (SUBLOAN) in Regression 5 and its decomposition (Regression 6‐8)

into subsidized equity (SUBEQ), subsidized borrowing (SUBBOR) and revenue grants (RG) gives

contrasting but interesting results. Unlike Regression 1, when the quadratic form is not included in

Regression 5, the relationship between efficiency and subsidization becomes positive as evident

from the positive linear term of SUBLOAN though the impact is insignificant. In Regression 6 and 8,

subsidized equity (SUBEQ) and revenue grants (RG) does not explain any variation in the efficiency

as their coefficients are insignificant. However in Regression 7, subsidized borrowings (SUBBOR)

do explain significant variation in the efficiency of MFIs. Its significant positive coefficient depicts

that the subsidized borrowing contributes towards efficiency of MFIs. The regression equations

containing the quadratic terms for all the components of subsidy have also been investigated

(though not presented in the paper) but the impact of both linear and quadratic terms of subsidy

on the efficiency remains insignificant.

Our regression analysis show that lending to women does not explain any variation in

efficiency of microfinance as its positive coefficient is insignificant in all the regressions. Similarly,

no significant relationship has been found between the outreach and efficiency contrary to

Hermes et al., (forthcoming) who found that outreach and efficiency are negatively correlated.

Further, no impact of the ownership status of the MFI has been found on the efficiency. This

would mean that it is not because an MFI is registered as a cooperative, rural bank, NGO or bank

that it is more or less efficient. On the other hand, some evidence has been found that MFIs

offering other services in addition to the provision of credit (credit‐plus) are found to be efficient

only at 10% level in Regression 6 and 8 when subsidized equity and revenue grants explain

variation in efficiency of MFIs respectively. Which depicts that MFIs exhort subsidized funds in the

form of equity and revenue grants are more likely to be efficient in credit‐plus

activities. Interestingly, we have found evidence that MFIs which are regulated are more efficient

in most of the regressions. Thus depicts that MFIs (mostly banks and NBFI status) which are

subject to prudential regulation and supervision are more efficient unlike institutions relying on

other people’s money (e.g. donor‐supported NGOs) which are legally registered, but not regulated

or supervised. For other controls, MFIs lending methodology does not explain any variation in

efficiency. However, much in line with the reality, MFIs located in the Latin America along with

those in CA & EE region are found to be the efficient one unlike their counterparts in South Asia,

which are found to be inefficient.

7. Conclusion

At the outset of this paper, we endeavored to resolve few key issues. How to incorporate

the subsidies into the non parametric DEA framework to investigate the efficiency of microfinance

institutions? What factors are important in determining the efficiency of microfinance and how

much of these factors are driven by the subsidies in determining the efficiency of MFIs? In other

words, How efficiency relates to the various organizational and structural variables amid the

presence of subsidies? The way subsidy has been calculated in this paper, by summing the

subsidized equity, subsidized borrowing and revenue grants, allows us to compare the impact of

different components of subsidies on the efficiency of MFIs. By employing non‐parametric

efficiency framework, a comparison of efficiency scores with and without subsidies depict that the

average efficiency scores are improved when subsidies enter into the DEA framework. Specifically,

there exist numbers of MFIs which become 100% efficient with subsidies and conversely, there

exist MFIs which were previously 100% efficient but become less efficient once subsidies have

been accounted for. The issue of the impact of subsidization on the efficiency in the presence of

several organizational, social and structural variables has been addressed by employing random‐

effect Tobit model.

We find some evidence of positive association between subsidy dependence and efficiency

but it is only established for subsidized borrowing. Nevertheless, no significant effect is found for

total subsidies, subsidized equity and revenue grants. This suggests that subsidizing credit lines

would have better impact in terms of efficiency rather than grants or subsidized equity. Even with

the inclusion of quadratic term of subsidies, the relationship remains insignificant for all the

regressions up to the threshold level after which more subsidization contributes towards

efficiency. Only once we drop the size variable, the relationship become negative and significant

up to the threshold level. The size of the MFI is a major driver of efficiency, MFIs serving a large

number of clients are particularly more efficient that smaller ones. From social perspective,

lending to women borrowers and MFIs outreach do not explain any variations in efficiency.

However we do find evidence that those MFIs, which are regulated and provide credit‐plus

services, are more efficient.

This essay adds to the existing literature by taking on the issue of subsidies for the first

time in evaluating the efficiency of microfinance based on the data of 170 MFIs worldwide. From

policy perspective, valuable lessons can be drawn for the entire stakeholder in microfinance

Industry on the basis of this research work. For microfinance practitioners, it serves as a

performance evaluation guide to enhance the efficiency and in the course of that meeting the dual

objectives of outreach and sustainability. A with and without subsidy analysis based on the

efficiency scores of their respective MFIs can help them identify the efficiency‐enhancing role of

subsidies. In particular, the message is clear for those socially driven MFIs cater to poor and

women borrowers, to devise new income enhancing and enterprise development schemes which

can go a long way in enhancing their efficiency. From private investor’s perspective, it identifies

those MFIs which are successful in achieving maximum efficiency by a proper mix of inputs and

outputs. Even the social investors can benefit by analyzing mission‐driven MFIs in the sample

which are successful in increasing their outreach. For academia and researchers, this research

opens a new avenue of research by examining the role of subsidies on the efficiency of

microfinance in both non‐parametric and parametric framework.

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Table 2: Variable Description and Summary Statistics

Subsidized Funds Obs Definition Unit Mean Med. Min Max

Total Subsidy/Loan Portfolio The sum of revenue grants, subsidised equity

and concessionary (subsidised) borrowings $ 0.1423 0.0768 0.001 3.46

Subsidized Equity/ Loan

Portfolio 340

Equity grants are the sum of direct grants DG

and paid‐in capital PC. $ 0.0720 0.0489 0.0005 0.8758

Subsidized Borrowings/ Loan

Portfolio 340

Concessionary loans multiplied by the difference

between market interest rate and what MFIs

actually pay.

$ 0.0374 0.0187 ‐0.0424 1.9006

Revenue Grants (RG)/ Loan

Portfolio 340

Cash gifts except for the accounting choice to

record them as revenues rather than as direct

injection to equity.

$ 0.0329 0 0 1.2201

Inputs & Outputs Obs Definition Unit Mean Med. Min Max

Total asset (A) 340

Average of current year (t) and previous year (t‐

1) assets. It includes all asset accounts net of all

contra‐asset accounts, such as the loan‐loss

allowance and accumulated depreciation.

$(000s) 46000 16000 328 566100

Operational cost (C) 340

Expenses related to operations, such as all

personnel expenses, rent and utilities,

transportation, office supplies, and depreciation

$(000s) 4700 2100 96 77300

Staff (E) 340 The number of individuals who are actively

employed by the MFI. No. 620 221 9 24457

Total Subsidy (S) 340 The sum of revenue grants, subsidised equity

and concessionary (subsidised) borrowings $(000s) 2700 1100 6.583 112700

Outstanding Loan Portfolio

(LP) 340

The outstanding principal balance of all of an

MFI’s outstanding loans $(000s) 33000 11000 272 328700

Financial Revenues 340

Revenue generated from the gross loan portfolio

(R) and from investments plus other operating

revenue

$(000s) 9200 3500 71 150000

Organizational variables

Operational Self

Sufficiency(OSS) 340

Financial Revenue (Total)/ (Financial expense +

Loan loss provision expense + Operating

expense)

(%) 124.36 121.44 3.57 254.88

Loansize/GNIpc 340 Average loan size/ GNI per capita $ 0.9438 0.4225 0.0248 31.34

GNI per capita (current) 340 Gross national income divided by the population. $ 1404 1090 160 6070

MFI age 340 The years since MFI has started operations No. 13.92 12 3 51

Borrowers 340

The number of individuals who currently have an

outstanding loan balance with the MFI or are

responsible for repaying any portion of the Gross

No. 112366 26516 949 5163279

Loan Portfolio

Women borrowers 3304 Percentage of borrowers who are women % 64.12 60.6 8.6 102.1

Source: Own calculations

Table 3: Average Efficiency Scores (vrs)

Region Status Lending

methodology Regulated

Savings Providers

Other services

Africa 0.6638 Bank 0.6459 I 0.7048 Not 0.7046 Not 0.6809 Not 0.6852

South Asia 0.6475 Coop. 0.8406 I & S 0.6532 Yes 0.6539 Yes 0.6708 Yes 0.6573

Latin America 0.6944 NBFI 0.6124 S 0.6154

ME & NA 0.5620 NGO 0.7256 V 0.7664

CA & EE 0.7234 R. Bank 0.5298

EA & P 0.6483

Source: Own calculations Average scores are based on total of 340 observations.

4 10 observations have been reduced because five MFIs have no women borrowers information available.

Table 4: Correlation Matrix T. Sub SubEq SubBor RG OSS Age Women Outreach Borr. L. port Rev. Asset Cost Staff Region Status Lending Saving Regulat Other

T. Sub 1.000

SubEq 0.768** 1.000

SubBor 0.798** 0.483** 1.000

RG 0.724** 0.334** 0.310** 1.000

OSS ‐0.259** ‐0.140** ‐0.139* ‐0.31** 1.000

Age ‐0.122* ‐0.126* ‐0.054 ‐0.104 0.071 1.000

Women 0.137** 0.075 0.081 0.156** ‐0.093 0.136* 1.000

Outreach ‐0.034 0.010 ‐0.011 ‐0.073 0.066 ‐0.150** ‐0.297** 1.000

Borr. 0.004 ‐0.023 ‐0.037 0.068 0.279** 0.255** 0.137* ‐0.058 1.000

Input‐output Efficiency Variables

L. port ‐0.141* ‐0.186** ‐0.071 ‐0.079 0.201** 0.202** ‐0.138* 0.036 0.581** 1.000

Rev. ‐0.0809 ‐0.1301 ‐0.024 ‐0.041 0.136 ‐0.189** ‐0.095 0.008 0.539** 0.941** 1.000

Asset ‐0.126* ‐0.173** ‐0.071 ‐0.055 0.167** 0.222* ‐0.143** 0.050 0.588** 0.966** 0.915** 1.000

Cost ‐0.063 ‐0.130* 0.004 ‐0.032 0.031 0.164** ‐0.102 0.027 0.413** 0.905** 0.956** 0.898** 1.000

Staff 0.000 ‐0.038 ‐0.041 0.078 0.228** 0.260** 0.123* ‐0.004 0.972** 0.634** 0.602** 0.659** 0.487** 1.000

Categorical Variables

Region ‐0.090 ‐0.113* ‐0.070 ‐0.028 0.114* 0.124* 0.279** ‐0.176** 0.226** 0.140** 0.136 0.112* 0.091 0.225* 1.000

Status 0.123* 0.083 0.074 0.124* 0.095 0.267** 0.546** ‐0.326** 0.070 ‐0.341** ‐0.299* ‐0.371** ‐0.362** 0.011 0.171** 1.000

Lending 0.123* 0.125* 0.013 0.153** ‐0.166** 0.012 0.544** ‐0.228** ‐0.103 ‐0.293** ‐0.264* ‐0.297** ‐0.242** ‐0.120* 0.098 0.348** 1.000

Saving ‐0.102 ‐0.143** ‐0.016 ‐0.086 ‐0.077 0.162** ‐0.100 0.127* 0.111* 0.224** 0.207** 0.264** 0.235** 0.149* ‐0.167** ‐0.389** ‐0.098 1.000

Regulated ‐0.056 ‐0.047 0.072 ‐0.159** 0.044 ‐0.079 ‐0.258** 0.184** 0.005 0.161** 0.155** 0.192** 0.185** 0.036 ‐0.176** ‐0.370** ‐0.238** 0.336** 1.000

Other 0.099 0.063 0.090 0.071 0.019 0.103 0.389** ‐0.152** 0.168** ‐0.018 ‐0.005 ‐0.028 ‐0.028 0.156* 0.252** 0.345** 0.256** ‐0.167** ‐0.226** 1.000

Source: Authors own calculations. Total No. of observations are 340. *Significance level at 5%; **Significance level at 1%

Table 5: Efficiency Analysis (With and Without Subsidies)

LR‐ACE Wilcoxon

Signed ranked test

R‐ACE Wilcoxon

Signed ranked test

technical efficiency

with subsidies

without subsidies

with

subsidies without subsidies

constant (CRSTE)

0.582 0.528 8.254** p>0.000

0.480 0.266 13.697** p>0.000

variable (VRSTE)

0.688 0.634 7.193** p>0.000

0.566 0.288 13.905** p>0.000

scale efficiency (SE)

0.831 0.806 7.954** p>0.000

0.840 0.753 9.613** p>0.000

Source: Author’s own calculations. All values are average efficiencies of the total MFIs.

Table 6: Tobit Regression Analysis (Random Effect Model) Dependent variable: Efficiency (LR‐ACE)

z‐values in parentheses *significant at 10%; ** significant at 5%; *** significant at 1%. Source: Authors calculations based on data compiled from the audit reports of MFIs and from the Mix Market website.

(1) (2) (3) (4) (5) (6) (7) (8)

LogAge ‐0.070 (‐1.30)

0.014 (0.27)

‐0.065 (‐1.20)

‐0.067 (‐1.24)

‐0.070 (‐1.29)

‐0.065 (‐1.20)

LogOSS 0.051 (0.65)

0.099 (1.24)

0.053 (0.67)

0.035 (0.45)

0.054 (0.69)

0.046 (0.56)

LogBorrower 0.086

(4.02)*** 0.077

(3.96)*** 0.082

(4.22)*** 0.089

(4.20)*** 0.084

(3.85)*** 0.093

(4.38)*** 0.085

(4.08)*** Total Subsidy/loan (SUBLOAN) Subsidized Equity/loan(SUBEQ)

Subsidized borrowing/loan (SUBBOR)

Revenue Grants/loan (RG)

‐0.204 (‐1.08)

‐0.345 (‐1.82)*

‐0.204 (‐1.09)

0.089 (0.98)

0.099 (1.06)

‐0.070 (‐0.29)

0.382 (2.02)**

0.090 (0.42)

SUBLOAN‐sqr 0.123 (1.85)*

0.150 (2.21)**

0.120 (1.79)*

Loan Size/GNIpc ‐0.008 (‐0.26)

‐0.035 (‐1.18)

‐0.008 (‐0.29)

‐0.007 (‐0.23)

‐0.006 (‐0.20)

‐0.010 (‐0.35)

‐0.005 (‐0.18)

‐0.008 (‐0.28)

Women Borrower 0.016 (0.23)

0.025 (0.35)

0.008 (0.11)

0.012 (0.17)

0.020 (0.28)

0.020 (0.28)

0.017 (0.25)

0.020 (0.28)

Bank ‐0.069 (‐0.90)

‐0.031 (‐0.40)

‐0.065 (‐0.87)

‐0.070 (‐0.94)

‐0.073 (‐0.95)

‐0.069 (‐0.89)

‐0.073 (‐0.95)

‐0.069 (‐0.89)

Cooperatives 0.129 (1.11)

0.083 (0.70)

0.099 (0.86)

0.117 (1.02)

0.147 (1.26)

0.130 (1.12)

0.153 (1.33)

0.137 (1.18)

NGOs ‐0.027 (‐0.43)

‐0.048 (‐0.74)

‐0.038 (‐0.60)

‐0.038 (‐0.61)

‐0.029 (‐0.45)

‐0.021 (‐0.33)

‐0.037 (‐0.59)

‐0.023 (‐0.36)

Rural Bank 0.054 (0.33)

‐0.156 (‐0.96)

‐0.002 (‐0.01)

0.015 (0.09)

0.067 (0.40)

0.060 (0.35)

0.075 (0.45)

0.061 (0.37)

Individual 0.113 (1.13)

0.130 (1.39)

0.107 (1.16)

0.114 (1.24)

0.120 (1.30)

0.122 (1.32)

0.124 (1.35)

0.118 (1.27)

Individual & Group 0.016 (0.20)

0.048 (0.58)

0.010 (0.13)

0.019 (0.23)

0.024 (0.30)

0.029 (0.35)

0.022 (0.28)

0.025 (0.30)

Village Banking ‐0.099 (‐0.89)

‐0.063 (‐0.55)

‐0.092 (‐0.83)

‐0.108 (‐0.97)

‐0.115 (‐1.03)

‐0.108 (‐0.96)

‐0.098 (‐0.89)

‐0.115 (‐1.02)

C.Asia & E.Europe 0.178 (2.01)**

0.124 (1.39)

0.194 (2.22)**

0.199 (2.27)**

0.183 (2.07)**

0.172 (1.94)*

0.174 (1.97)**

0.179 (2.01)**

E. Asia & Pacific ‐0.016 (‐0.16)

‐0.040 (‐0.40)

‐0.026 (‐0.26)

‐0.026 (‐0.26)

‐0.016 (‐0.16)

‐0.029 (‐0.29)

‐0.014 (‐0.15)

‐0.023 (‐0.23)

Latin America 0.231

(3.36)*** 0.199

(2.86)*** 0.218

(3.26)*** 0.228

(3.40)*** 0.240

(3.48)*** 0.229

(3.33)*** 0.229

(3.36)*** 0.237

(3.38)***

M. East & N. Africa ‐0.135 (‐1.14)

‐0.136 (‐1.11)

‐0.104 (‐0.89)

‐0.109 (‐0.93)

‐0.139 (‐1.17)

‐0.150 (‐1.25)

‐0.129 (‐1.08)

‐0.149 (‐1.24)

S. Asia ‐0.153 (‐1.70)*

‐0.039 (‐0.45)

‐0.129 (‐1.46)

‐0.131 (‐1.47)

‐0.154 (‐1.70)*

‐0.163 (‐1.80)*

‐0.153 (‐1.71)*

‐0.159 (‐1.77)*

Other Services 0.080 (1.51)

0.082 (1.51)

0.072 (1.36)

0.076 (1.45)

0.084 (1.59)

0.089 (1.67)*

0.079 (1.50)

0.088 (1.66)*

Savings ‐0.077 (‐1.22)

‐0.050 (‐0.77)

‐0.078 (‐1.24)

‐0.068 (‐1.08)

‐0.066 (‐1.05)

‐0.067 (‐1.07)

‐0.073 (‐1.17)

‐0.066 (‐1.05)

Regulated 0.081 (1.49)

0.112 (2.03)**

0.087 (1.60)

0.093 (1.69)*

0.087 (1.60)

0.093 (1.69)*

0.072 (1.31)

0.094 (1.71)*

Constant ‐1.626

(‐3.26)*** ‐1.266

(‐2.52)***‐1.424

(‐4.06)***‐1.537

(‐4.43)***‐1.753

(‐3.53)***‐1.583

(‐3.23)*** ‐1.744

(‐3.62)*** ‐1.671

(‐3.35)***

No of Observations 330 330 330 330 330 330 330 330 No of Groups 165 165 165 165 165 165 165 165 Log Likelihood ‐154.42 ‐162.30 ‐155.45 ‐157.05 ‐156.12 ‐156.63 ‐154.64 ‐156.59 Wald Chi‐Square 73.25 54.45 70.72 66.87 69.12 67.88 72.72 67.99 Prob > Chi‐Square 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Documents

Efficiency of Microfinance: Do Subsidies Matter? · 2020. 1. 21. · Caudill et al. (2009), Cull et al. (2009) and Hudon and Traca (forthcoming) incorporate measures of subsidies