PROBING ON THE EFFICIENCY AND SUSTAINABILITY STATUS OF INDIAN
MICROFINANCING INSTITUTIONS: A DATA ENVELOPMENT ANALYSIS
APPLICATION
Nadiya M1 and T Radha Ramanan2
Abstract
This paper aims to probe on the efficiency and sustainability status of Indian microfinancing
institutions (MFIs). The intention is to identify a set of efficient and sustainable Indian MFIs,
whose best practices are worthy of emulation for the rest of the inefficient MFIs in the sample.
With the above aim it uses data envelopment analysis (DEA) technique to probe on the
efficiency status of a sample of 88 Indian MFIs. It uses Operational Self-Sustainability ratio and
Gow’s parameter to assess the sustainability status of these efficient MFIs. It identifies 14 Indian
MFIs to be efficient and 12 out of them to be sustainable. Inefficient MFIs in the sample are
expected to optimize its operations, by emulating the input minimisation and output
maximisation practices adopted by these 12 efficient
and sustainable MFIs. Such optimization of operations will facilitate Indian microfinance market
to strike a fair and reasonable interest rate, which is affordable to the poor and cost-covering for
the MFIs. As per the DEA analysis conducted, to optimize the operations of Indian microfinance
market 18.3 percent of inputs could be decreased without affecting the existing output levels and
20.2 percent of outputs to be increased without affecting existing levels of inputs. Further
research in terms of exploring the best practices of the identified 12 MFIs is recommended to
1
Corresponding Author: Nadiya M, Doctoral Student at School of Management Studies, National Institute of
Technology, Calicut, India. Kerala, India. Ph: +91 9746304036. Email: nadiya_pms09@nitc.ac.in
2
Dr T Radha Ramanan, Assistant Professor at Department of Mechanical Engineering, National Institute of
Technology, Calicut, India.
achieve the goal of optimized operations. The present study serves as the first step in this
direction, by identifying a set of efficient and sustainable MFIs in India.
Introduction
Hitherto the crisis in the Indian microfinance sector3, a growing loan portfolio that earns a
financial surplus for a Microfinancing Institution (MFI), was regarded as the key performance
indicator of its sustainability. But the crisis proved that the growth is not always a positive sign.
Growth can be counterproductive, if not managed efficiently. For in the name of growth and
sustainability, most MFIs have started to levy exorbitant interest rates from the poor, thereby
passing on the inefficiency burden to the poor (Singh, 2010). This has attracted regulatory
attention4. Regulators disdain this practice as being against the spiritual foundation of an industry
that caters to the needs of the economically weak. But Indian MFI practitioners argue, levying
cost-covering interest rates to be imperative for the sustainability of its microfinance operations.
(Mahajan, 2010).
This contentious tussle between regulators and practitioners sets context for this paper. The
authors share the sentiments of both the stakeholders—Regulators and MFI Practitioners. As it
is important for the regulators to protect the poor from usurious interest rates, so it is for the MFI
practitioners to protect their institution from losses. A reasonable interest rate, that is affordable
3
A spate of suicides among the microfinance clients in the district of Andhra Pradesh, during
the month of September, 2010, allegedly due to exorbitant interest rates and the coercive
recovery practices adopted by some of the Microfinancing Institution (MFIs) have resulted in a
crisis in Indian Microfinance Industry. For more details see, Intellecap (2010); Swami,P., Shekar,
M., & Choksey, N. (2010).
4
Andhra Pradesh Government promulgated an ordinance on October 15, 2010, followed by bill
passed on the same on December 14,2010.
For more details see, Andhra Pradesh Microfinance Institutions (Regulation of Money Lending)
Ordinance (2010).
for the poor and cost-covering for the MFIs, will satisfy both the stakeholders. But microfinance
markets are yet to strike such a fair and reasonable price. Practitioners opine that such a
reasonable interest rate can be arrived at only by the most efficient MFIs in the market 5. A MFI
can be regarded efficient, if it spends equal amount of money on resources as other MFIs, but
generates higher levels of performance, and if it spends less amount of money on resources to
generate same level of performance as other MFIs, in the microfinance industry. In effect, an
efficient MFI maximizes output and minimizes input in its basic operation of loan disbursement.
Attaining such efficiency in operations as a prerequisite to MFI’s growth and sustainability, is
imperative for Indian microfinance market to charge a fair and reasonable interest rate from the
poor [Microfinance Insights, 2010].
Against this backdrop, this papers aims to explore the existence of a set of efficient and
sustainable MFIs in India, whose best practices can be emulated by the rest of the sample MFIs
operating in Indian microfinance industry. To serve this purpose the foremost intent of this work,
is to identify a set of efficient Indian MFIs, by determining the relative efficiency scores of a
sample of 88 Indian MFIs, using a data envelopment analysis model. Thereafter, out of these
efficient MFIs the sustainable ones are ascertained using the Operation Self- Sustainability ratio
and Scale parameter. Thus by resorting to this filtering exercise, the paper fulfills its objective of
identifying a set of efficient and sustainable MFIs, which are worthy of emulation for other
5
According to MFI practitioners, a reasonable interest rate can be charged by only those efficient
MFIs, which minimizes the three components of its interest rate, i.e.—cost of funds, delinquency
rates and operating costs—and maximizes its outreach, i.e.—loan portfolio. In India, MFIs have
wide variations in all these aspects. This clearly depicts that depending on the operational
efficiency of Indian MFIs, the interest rates charged by it, can be reduced. For more details see,
[Microfinance Insights, 2010].
inefficient Indian MFIs in the sample. The paper concludes by exhorting further research in the
lines of understanding the best practices used by these efficient and sustainable MFIs for input
minimization and output maximization.
The rest of the paper is structured as follows. The next section covers a brief literature review on
efficiency and sustainability measurement of MFIs conducted so far using DEA method. Section
2 discusses the methodology and contribution of the paper. Section 3 presents the data and input-
output specifications for the DEA model used in the paper. Section 4 presents the empirical
analysis and discusses on the results of the efficiency analysis and sustainability assessment
undertaken in the study. Based on the results a set of efficient and sustainable Indian MFIs are
identified in this section. Finally section 6 draws a summary and conclusion for the work.
Literature Review
There is a dearth of literature concerning the analysis of MFI’s efficiency using DEA across the
world. Authors like Farrington (2000) and Lafourcade, Isern, Mwangi & Brown (2005) has used
ratio analysis technique for MFI efficiency measurement. Stochastic Frontier Analysis, a
parametric method is used by authors like Hassan & Tufte (2001) and Desrochers & Lamberte
(2003) for efficiency analysis. But both ratio analysis and stochastic frontier analysis techniques
has limitations in using multiple inputs and multiple outputs for estimating the joint efficiency of
MFIs. This can be effectively done by DEA, a non-parametric method that do not impose a priori
functional form for production technology. Despite this advantage, DEA is used only in a
handful of studies, to examine the efficiency of MFIs. Some attempts made across the world, in
this direction are identified and listed below in table 1.
Table 1 Literature Review on MFI Efficiency Analysis Done Using DEA method
Author & Year MFI Region Input and Output Findings
Specification
Ngheim, Coelli & Rao 46 MFIs in Vietnam Inputs: Labour cost MFIs are found
(2006) and Non-Labour costs technically efficient,
Outputs: Number of with an average
savers, Number of technical efficiency
borrowers score of 80 percent.
and Number of groups Age & Location of
MFI are found to have
a significant influence
upon efficiency
scores.
Gutierrez-Nieto, Cinca & 30 MFIs in Latin Inputs: Number of Using multivariate
Molinero (2006) America credit officers and analysis, efficiency is
Operating expenses found to be affected
Outputs: Number of by country effect and
loans outstanding, status of MFI.
Gross loan portfolio
and Interest and fee
income
Qayyum & Ahmad (2006) 85 MFIs in South Inputs: Credit officers The study attributes
Asia ( 15 Pakistani, and Cost per borrower inefficiencies in the
25 Indian and 45 Output: Loans three South Asian
Bangladeshi MFIs) disbursed by MFI regions to be technical
in nature, which calls
for more managerial
and technological
improvements.
Sufian(2006) 20 MFIs in Malaysia Inputs: Total Deposits The study observed
and Fixed Assets only 28.75 percent of
Outputs: Total Loans all Malaysian MFIs to
and Other Income be efficient and more
profitable. Size and
market share are
found to have a
negative effect on
efficiency.
Bassem(2008) 35 MFIs in the Inputs: Personnel and Eight MFIs in the
Mediterranean Total Assets region are found
Outputs: Number of technically efficient.
women borrowers and Size of MFI is found
Return on Assets to have a negative
effect on efficiency.
Haq, Skully & Pathan 39 MFIs across Inputs: Labour, Cost Results showed non-
(2009) Africa, Asia & Latin per borrower governmental MFIs
America and Cost per saver to be efficient under
Outputs: Savers per production approach
staff member and and bank-MFIs to be
borrowers per efficient under
Staff member. intermediation
approach. The study
concludes that in the
long-run bank-MFIs
will outperform non-
governmental MFIs,
as they have more
access to local capital
market.
Out of the above discussed works only Qayyum & Ahmad (2006) follows up the DEA efficiency
analysis with a sustainability assessment using scale parameter.
Methodology & Contribution of the Paper
Similar to the papers discussed in the literature review section, this paper begins by adopting a
non-parametric DEA methodology. DEA is a linear programming methodology, popularized by
Charnes, Cooper & Rhodes (1978), by building on the efficiency ideas put forth by Farrell
(1957).
This method is widely accepted among strategic, policy and operational circles, particularly in
the service and nonprofit sectors. Its wide acceptance is due to its ability, to estimate efficiency
scores for complex multi-input or multi-output firms, where the underlying production process is
not well understood. Since this paper intends to assess the relative efficiency scores of Indian
MFIs, whose production process cannot be analytically represented, the DEA method was found
most suitable for this purpose.
In this paper, both the models of DEA—the Constants Returns to Scale Model, called Charnes,
Cooper & Rhodes Model and the Variable Returns to Scale Model, called Bankers, Charners &
Cooper Model—under both input-oriented and out-put-oriented versions, are used (Charnes,
Cooper & Rhodes, 1978; Bankers, Charners & Cooper, 1984). Using these models the study
identifies the extent to which Indian MFIs can reduce its inputs without affecting its output levels
and the extent to which they can increase its outputs without affecting its existing input levels.
Across these models, the MFIs which have merged most efficient are identified. Subsequently,
using operational self-sustainability ratio and scale parameters the sustainable MFIs among these
efficient MFIs are identified. Finally the study narrows down to a set of efficient and sustainable
MFIs. Thus the study enables the inefficient Indian MFIs in the sample to identify a set of
efficient and sustainable set of MFIs, whose best practices it can emulate for input minimization
and output maximization.
As depicted in Section 1, apart from Qayyum & Ahmad’s (2006) paper that ranks 25 Indian
MFIs using DEA method, there has been no other study made in this direction. This paper
contributes to literature by undertaking a comprehensive DEA benchmarking analysis among
Indian MFIs. The DEA model used in the study is more comprehensive in the sense that it
recognizes both the social and financial goal of a MFI, while specifying the input-output choices.
Such a model is used to identify the efficient MFIs in Indian context. Thereafter the sustainable
MFIs among these efficient MFIs are identified. Thus from an extended sample of 88 Indian
MFIs, this study identifies a set of efficient and sustainable set of MFIs, whose practices are
worthy of emulation for the rest of the inefficient MFIs in the sample.
Sample Data and Specification of Inputs and Outputs for the DEA Model
For the purpose of this study a sample of 88 Indian MFIs is used. These 88 MFIs are the only set
of Indian MFIs that have reported their financial data to Microfinance Information Exchange
database for the year 2009.
Since the inputs and outputs specification for the DEA model has to be in conformity with this
approach chosen for doing a DEA, first the DEA approaches applicable to financial institutions
are identified. Berger & Humphrey (1997) suggests two approaches—production approach and
financial intermediation approach—to be commonly used for efficiency analysis among financial
institutions. The approach chosen for efficiency analysis in these financial institutions depends
upon what these institutions actually do.
Going by this logic, the authors try to portray what MFIs do under each of these approaches. In a
pure production approach a MFI is assumed to be producers of loans and deposits. That is, in this
approach loans and deposits are treated as outputs, with labour and other capital resources
forming the inputs (Soteriou & Zenios,1999; Vassiloglou & Giokas, 1990). But in a pure
financial intermediary approach a MFI is assumed to be an intermediary who makes profits by
matching depositors and borrowers in a financial market. In this approach, deposits are treated as
inputs, with a surplus generation as output (Berger & Mester, 1997; Athanassoupoulos, 1997)
Thus it is noted that deposits are treated in two different manner under these two approaches.
This is not a concern in this study as only limited number of Indian MFIs (only licensed Non-
Banking Financial Companies, which have investment-grade credit rating), are permitted to raise
deposits in India. Thus as deposits do not constitute a homogeneous variable across all MFIs, it
do not feature as an input or output for this study6.
Since deposits do not constitute a variable for this study, either a pure production approach or
financial intermediation approach could not be adopted. Thus similar to Guitierrez-Nieto,
Serrano-Cinca,, & Molinero (2007), a mixture of both these approaches is adopted in this study.
The DEA model proposed in this study views MFIs as financial institutions bound to keep its
dual goals—both social and financial (Woller, Christopher & Warner, 1999; Schreiner, 2002;
Guitierrez-Nieto, Serrano-Cinca & Molinero, 2008). Thus social and financial goals of a MFI
forms the outputs for the DEA model used in this study.
The social goal is denoted by depth of outreach i.e. the extend to which microfinance reaches the
poor. Depth of outreach can be captured by poverty level and gender of the clients (Christen,
2001; Navajas, Schreiner, Richard, Claudio & RodriguezMeza, 2000; Bhatt & Tang, 2001). The
assumptions are that the greater the number of poor clientele and women clientele served by
microfinance, the deeper is the outreach. Both these variables are included as outputs in the DEA
model, as per production approach.
The financial goal on the other hand is denoted by the MFI’s ability to generate a surplus on its
growing loan portfolio (Otero,2000; Robinson,2001).These are captured by the gross loan
6
DEA requires homogeneous data for all firms under study.
portfolio of an MFI and the interest and fee income charged by them 7. Gross loan portfolio is
included as an output in the model as per production approach and interest and fee income is
included as per intermediation approach.
The input specification in this model has three variables—total assets, number of credit officers
and cost per borrower. The former two variables are included as per production approach and the
latter as per intermediation approach. This is so as these variables serve as inputs for an MFI’s
operations, as per these respective DEA approaches.
Thus, the DEA model formulated is as follows.
FIGURE 1 DEA MODEL
INPUTS OUTPUTS
NUMBER OF
WOMEN
SOCIAL BORROWERS
TOTAL ASSETS
NUMBER OF POOR
BORROWERS
NUMBER OF MFI
CREDIT
OFFICERS
GROSS LOAN
PORTFOLIO
COST PER
BORROWER INTEREST AND
FEES INCOME
FINANCIAL
7
Charing cost covering interest rates is a means of furthering the financial goal of an MFI. But
as discussed earlier, if the MFI passes on its operational inefficiencies to the clients, in the form
of hiked interest rates, then it can prove counterproductive for its long run sustainability.
In this paper, the relative efficiency scores of Indian MFIs are assessed by testing this DEA
model. The relative efficiency score for MFIs are computed using Data Envelopment Analysis
Programme (DEAP), by comparing a given MFI to a pool of well-performing MFIs that serve as
a benchmark for the MFI under evaluation.
Data for all the variables in the model are sourced from the financial statements of the MFIs,
except for the figure for number of poor borrowers which is not readily available. The data for
number of poor borrowers was calculated from the value of Average Loan Size Per Capita Gross
National Income (GNI) , using the premise stated by Nieto, Cinca & Molinero (2008). The
premise is as follows: ―Given any two MFIs with identical inputs, the one that makes many small
loans (small relative to the country’s per capita GNI) will be more socially efficient that the one
that makes larger loans”. Based on this premise the equation used for deriving the poor
borrowers figure is as follows:
pi = Ki - Min (K)
Max (K) - Min (K)
P = pi * B
Where, K = Average Loan
Per Capita Gross National Income
pi = Proportion of Poor Borrowers, 0 < pi < 1
P = Number of Poor Borrowers
B = Total Number of Borrowers
Empirical Analysis & Results
The empirical analysis done can be categorized into two heads a) efficiency analysis and b)
sustainability assessment.
a) Efficiency Analysis
In this work, efficiency analysis is undertaken using DEA technique. DEA is performed using
input and output orientation versions under both Charnes, Cooper and Rhodes Model (CCR
Model) and Banker, Charnes and Cooper Model (BCC Model). The model formulation is
discussed in appendix.
The input orientation version depicts the minimization of inputs possible to produce specified
levels of outputs, whereas output orientation version depicts the maximization of outputs
possible with specified levels of inputs. The CCR model assumes constant returns to scale
relationship between inputs and outputs and calculates the overall efficiency for each unit, where
both pure technical efficiency and scale efficiency are aggregated into one value. Owing to this
assumption, this model will yield the same efficiency score regardless of whether it is input or
output orientated. But the BCC model which assumes variable returns to scale, calculates the
pure technical efficiency and gives two different technical efficiency score for the units, under
both input and output orientations. The efficiency scores derived as results from both these
models, under input and output orientation methods are presented in table 2
Table 2 DEA Efficiency Scores Computed Using Input and Output Orientation Versions
Under Constant Return to Scale and Variable Returns to Scale Assumptions (i.e. CCR and
BCC Models)
SL.NO. INPUT ORIENTATION OUTPUT ORIENTATION
MFIs
CRSTE8 VRSTE9 SCALE10 irs/drs11 CRSTE12 VRSTE13 SCALE14 irs/drs15
1 Adhikar 0.691 0.724 0.955 irs 0.691 0.707 0.977 irs
2 Arohan 0.747 0.756 0.988 irs 0.747 0.752 0.994 irs
3 ASA 0.669 0.682 0.98 irs 0.669 0.674 0.992 irs
4 Asirvad 0.794 0.823 0.964 irs 0.794 0.8 0.992 irs
5 Asmita 1 1 1 - 1 1 1 -
6 Asomi 0.627 0.673 0.932 irs 0.627 0.648 0.969 irs
7 AWS 0.674 0.897 0.751 irs 0.674 0.837 0.805 irs
8
CRSTE denotes the Constant Returns to Scale Technical Efficiency. It is the gross efficiency score produced by
CCR model under CRS assumption with input orientation. It comprises of scale efficiency and technical efficiency
aggregated into one.
9
VRSTE denotes the Variable Returns to Scale Technical Efficiency. It is the pure technical efficiency score
calculated by BCC model under VRS assumption and input orientation. It takes into account the variation in
technical efficiency with respect to scale of operation. A unit is said to be technically efficient if it minimizes input
per unit of output produced.
10
SCALE denotes the efficiency of a unit calculated when its size of operation is optimal, under input orientation.
Scale efficiency is calculated by dividing aggregate efficiency (from the CCR model) by technical efficiency (from
the BCC model).
11
irs denotes MFIs with increasing returns to scale and drs denotes MFIs with decreasing returns to scale.
12
CRSTE denotes the Constant Returns to Scale Technical Efficiency. This is the gross efficiency score produced
by CCR model under CRS assumption with output orientation. It comprises of scale efficiency and technical
efficiency aggregated into one.
13
VRSTE denotes the variable returns to scale technical efficiency. It is the pure technical efficiency score
calculated by BCC model under VRS assumption with output orientation. It takes into account the variation in
technical efficiency with respect to scale of operation. A unit is said to be technically efficient if it maximizes output
per unit of input used.
14
SCALE denotes the efficiency of a unit calculated when its size of operation is optimal, under output orientation.
Scale efficiency is calculated by dividing aggregate efficiency (from the CCR model) by technical efficiency (from
the BCC model).
15
irs denotes MFIs with increasing returns to scale and drs denotes MFIs with decreasing returns to scale.
8 Bandhan 1 1 1 - 1 1 1 -
9 BASIX 0.608 0.608 0.999 - 0.608 0.608 0.999 drs
10 BFL 0.647 0.683 0.946 irs 0.647 0.65 0.995 drs
11 BISWA 0.663 0.669 0.991 irs 0.663 0.665 0.997 irs
12 BJS 0.705 1 0.705 irs 0.705 1 0.705 irs
13 BSS 0.725 0.728 0.995 irs 0.725 0.726 0.999 irs
14 BWDC 0.699 1 0.699 irs 0.699 1 0.699 irs
15 Casphor MC 0.818 0.82 0.997 drs 0.818 0.824 0.993 drs
16 Chaitanya 0.288 0.587 0.49 irs 0.288 0.352 0.819 irs
17 Cresa 0.704 0.731 0.962 irs 0.704 0.712 0.988 irs
18 Disha 0.586 0.811 0.722 irs 0.586 0.7 0.837 irs
19 Equitas 0.915 1 0.915 drs 0.915 1 0.915 drs
20 ESAF 0.706 0.712 0.992 irs 0.706 0.708 0.997 irs
21 FFSL 0.932 0.986 0.945 drs 0.932 0.987 0.944 drs
22 GFSPL 0.886 0.89 0.996 irs 0.886 0.889 0.997 irs
23 GOF 0.678 0.71 0.955 irs 0.678 0.679 0.999 irs
24 GS 0.889 1 0.889 irs 0.889 1 0.889 irs
25 GSGSK 0.671 0.745 0.901 irs 0.671 0.715 0.94 irs
26 GTFS 0.629 1 0.629 irs 0.629 1 0.629 irs
27 GU 0.705 0.749 0.942 irs 0.705 0.732 0.963 irs
28 GV 0.835 0.836 0.999 drs 0.835 0.862 0.969 drs
29 HIH 0.347 0.357 0.972 irs 0.347 0.357 0.973 drs
30 ICNW 1 1 1 - 1 1 1 -
31 IDF 0.781 0.808 0.967 irs 0.781 0.798 0.979 irs
Financial
Services
32 India’s 0.667 0.685 0.974 irs 0.667 0.681 0.979 drs
Capital Trust
Ltd
33 INDUR 0.627 0.732 0.857 irs 0.627 0.679 0.923 irs
MACS
34 Janalakshmi 0.611 0.614 0.995 irs 0.611 0.612 0.999 irs
Financial
Services Pvt.
Ltd.
35 Janodaya 0.871 0.92 0.947 irs 0.871 0.909 0.959 irs
36 JFSL 0.714 0.748 0.956 irs 0.714 0.729 0.98 irs
37 KBSLAB 0.65 0.655 0.992 irs 0.65 0.652 0.997 irs
38 KOPSA 0.272 1 0.272 irs 0.272 1 0.272 irs
39 Kotalipura 0.975 1 0.975 irs 0.975 1 0.975 irs
40 Mahasakti 0.732 0.862 0.849 irs 0.732 0.832 0.879 irs
41 Mahesman 1 1 1 - 1 1 1 -
42 Mimo 0.732 0.733 0.999 drs 0.732 0.743 0.984 drs
Finance
43 MMFL 0.767 1 0.767 irs 0.767 1 0.767 irs
44 Nano 1 1 1 - 1 1 1 -
45 NBJK 0.685 0.893 0.767 irs 0.685 0.828 0.827 irs
46 NCS 0.72 0.884 0.815 irs 0.72 0.819 0.88 irs
47 NEED 0.745 0.805 0.926 irs 0.745 0.78 0.956 irs
48 NIDAN 0.237 1 0.237 irs 0.237 1 0.237 irs
49 Pustikar 1 1 1 - 1 1 1 -
50 PWMACS 0.669 0.753 0.889 irs 0.669 0.705 0.95 irs
51 RASS 0.913 0.918 0.994 irs 0.913 0.914 0.999 irs
52 RGVN 0.731 0.759 0.963 irs 0.731 0.748 0.977 irs
53 RISE 0.409 0.688 0.595 irs 0.409 0.535 0.765 irs
54 RORES 1 1 1 - 1 1 1 -
55 Saadhana 0.889 0.939 0.947 irs 0.889 0.931 0.956 irs
56 Sahara 0.679 0.687 0.988 irs 0.679 0.68 0.998 irs
Utsarga
57 Sahayata 0.73 0.731 0.998 drs 0.73 0.735 0.993 drs
58 Samasta 0.676 0.727 0.931 irs 0.676 0.706 0.958 irs
59 Sanchetna 0.476 0.753 0.632 irs 0.476 0.634 0.751 irs
60 Sanghamithra 1 1 1 - 1 1 1 -
61 Sarala 0.833 0.92 0.905 irs 0.833 0.903 0.922 irs
62 Sarvodaya 1 1 1 - 1 1 1 -
Nano
Finance
63 SCNL 0.64 0.642 0.996 irs 0.64 0.641 0.999 drs
64 SEIL 0.957 1 0.957 drs 0.957 1 0.957 drs
65 SEWA Bank 0.594 0.594 0.999 - 0.594 0.637 0.932 drs
66 SEWA 0.516 0.594 0.868 irs 0.516 0.519 0.993 irs
MACTS
67 SHARE 1 1 1 - 1 1 1 -
68 Share 0.686 0.761 0.902 irs 0.686 0.735 0.934 irs
MACTS
69 SKDRDP 1 1 1 - 1 1 1 -
70 SKS 1 1 1 - 1 1 1 -
71 Swayamshree 1 1 1 - 1 1 1 -
Micro Credit
Services
72 SMILE 0.717 0.727 0.987 irs 0.717 0.721 0.994 irs
73 SMSS 0.867 0.945 0.917 irs 0.867 0.924 0.937 irs
74 Sonata 0.605 0.623 0.971 irs 0.605 0.613 0.988 irs
75 Spandana 1 1 1 - 1 1 1 -
76 SSK 0.69 0.878 0.786 irs 0.69 0.826 0.835 irs
77 SU 0.93 0.957 0.972 irs 0.93 0.948 0.981 irs
78 Suryoday 0.554 0.587 0.944 irs 0.554 0.557 0.996 irs
79 SVCL 0.292 0.314 0.93 irs 0.292 0.36 0.81 drs
80 SVSDF 0.588 0.793 0.742 irs 0.588 0.696 0.845 irs
81 Swadhaar 0.482 0.494 0.975 irs 0.482 0.509 0.947 drs
82 SWAWS 0.965 1 0.965 irs 0.965 1 0.965 irs
83 Trident 0.769 0.774 0.994 irs 0.769 0.778 0.989 drs
Microfinance
84 UFSLP 0.69 0.836 0.825 irs 0.69 0.762 0.905 irs
85 Ujjivan 0.809 0.831 0.974 drs 0.809 0.839 0.964 drs
86 VFS 0.69 0.693 0.995 irs 0.69 0.69 1 -
87 VSSU 0.38 0.537 0.708 irs 0.38 0.397 0.957 irs
88 WSE 0.783 0.953 0.822 irs 0.783 0.925 0.846 irs
Mean 0.737 0.817 0.904 0.737 0.798 0.927
*Note: The 88 MFIs are those which has disclosed their financial data to Microfinance
Information Exchange (MIX) in the year 2009. More details on the identity of these MFIs can be
obtained at MIX Market website. http://www.mixmarket.org/mfi/country/India
Having derived the DEA efficiency scores, the number of MFIs appearing efficient across both
CCR and BCC models, under both input and output orientation methods, are assessed. Asmita,
Bandhan, ICNW, Mahesman, Nano, Pustikar, RORES, Sanghamithra, Sarvodaya Nano Finance,
SHARE, SKDRDP, SKS, Spandana and Swayamshree Micro Credit Services are found to be
efficient under both the methods as they reported an efficiency score of 100 per cent i.e a value
of 1. Thus 14 MFIs appeared efficient across all the models. The rest of the MFIs which has an
efficiency score of less than 1 are regarded relatively inefficient.
The average input oriented technical efficiency, pure technical efficiency and scale efficiency are
found to be 73.7, 81.7 and 90.4 respectively. The average output oriented technical efficiency,
pure technical efficiency and scale efficiency are 73.7, 79.8 and 92.7 respectively. Thus it is
concluded that 18.3 percent of inputs can be decreased without affecting the existing output
levels of Indian MFIs and 20.2 percent of outputs can be increased without affecting existing
levels of inputs of Indian MFIs. Moreover, the DEA analysis showed 71.5 percent of MFIs under
input oriented method and 63.6 percent under output oriented method as experiencing increasing
returns to scale and enjoying economies of scale.
The results are also used to assess the inefficiency status of Indian MFIs. The inefficiency scores
are calculated by taking the deviation of efficiency scores from unity. The mean inefficiency
score is derived as follows:
Mean Inefficiency Score = ∑ (1 - Ei )
N
Where Ei = Efficiency Score of ith MFI
N = Total Number of MFIs
As per the above formulae the pure technical inefficiency and pure scale inefficiency under input
oriented method is 16.26 percent and 8.52 percent respectively. Similarly the pure technical
inefficiency and pure scale inefficiency under output oriented method is 17.99 and 6.46 percent
respectively. Thus under both input and output oriented methods, the pure technical
inefficiencies of Indian MFIs are found to be greater than their pure scale inefficiencies.
b) Sustainability Assessment
In addition to the calculation of the efficiency scores of the MFIs, the analysis also does a
sustainability assessment on the identified 14 efficient MFIs. The intention is to identify a set of
Indian MFIs, which are efficient and at the same sustainable in its operations. Sustainability
assessment is done by examining the operational self-sustainability ratio and scale parameters of
the efficient MFIs. The operational self-sustainability ratio and scale parameters of the efficient
MFIs are depicted below in Table 2.
Table 2 Sustainability Metrics of the 14 Efficient MFIs
SL. NO. MFI OPERATIONAL SELF- SCALE OR
SUSTIANBILITY NUMBER OF
RATIO ACTIVE
BORROWERS
1 Asmita 146.66 per cent 1,340,288
2 Bandhan 158.30 per cent 2,301,433
3 ICNW 123.45 per cent 250,834
4 Mahesman 102.02 per cent 98,197
5 Nano 116.25 per cent 6,970
6 Pustikar 141.58 per cent 9,407
7 RORES 135.65 per cent 26,238
8 Sangamithra 119.13 per cent 118,807
9 Sarvodaya Nano 104.72 per cent 147,122
Finance
10 SHARE 154.94 per cent 2,357,456
11 SKDRDP 112.70 per cent 1,225,570
12 SKS 138.88 per cent 5,795,028
13 Spandana 180.04per cent 3,662,846
14 Swayamshree Micro 112.10 per cent 46,105
Credit Services
As per the Consultative Group to Assist the Poor (CGAP) and Microfinance Information
Exchange (MIX) standards, an Operational Self-sustainability ratio of 100 per cent and above
denotes the operational sustainability of a MFI. It denotes that an MFI has enough revenue to
cover its cost of funds, operational cost and loan loss provisions. As per Gow (2006) a scale
parameter of above 10,000 active borrowers denotes sustainability of a MFI. Taking these two
metrics into account, only 12 out of the 14 efficient MFIs are found sustainable. All 14 of the
efficient MFIs have an operational self-sustainability ratio of above 100 per cent. But two of the
efficient MFIs—Nano and Pustikar—did not comply with the scale parameter of 10,000 active
borrowers.
Thus the efficient and sustainable MFIs are 12 in number. They are Asmita, Bandhan, ICNW,
Mahesman, RORES, Sanghamithra, Sarvodaya Nano Finance, SHARE, SKDRDP, SKS,
Spandana and Swayamshree Micro Credit Services.
Further research in similar lines is recommended to understand the actual managerial best
practices used by these 12 efficient and sustainable MFIs, for input minimisation and output
maximisation. Thus by understanding and emulating the best practices of these MFIs, the other
relatively inefficient MFIs are expected to optimize their operations. This in turn will equip them
to charge reasonable cost-covering interest rates, without passing on the inefficiency burden to
the poor.
Summary & Conclusion
In this paper, the authors probe on the efficiency and sustainability status of Indian
microfinancing institutions (MFIs). The relative efficiency status of a sample of 88 Indian MFIs
is assessed using a DEA method. The DEA model used acknowledges both the social and
financial goals of a MFI. The DEA analysis reports 18.3 percent of inputs could be decreased
without affecting the existing output levels of Indian MFIs and 20.2 percent of outputs could be
increased without affecting existing levels of inputs of Indian MFIs. The model showed pure
technical inefficiency to be higher among the sample MFIs than pure scale inefficiency. The
results also depicted 14 Indian MFIs to be efficient under CCR and BCC models, with both input
and output orientation. Further a sustainability assessment revealed that 12 out of these 14
efficient are actually sustainable, as per operational self-sustainability ratio and scale parameters.
Based on this finding, the authors exhort the inefficient MFIs in India, to understand and emulate
the best practices of these 12 efficient and sustainable MFIs. This will require further research in
this direction. It will enable the inefficient Indian MFIs to enhance their efficiency and
sustainability status, which in turn will enable Indian microfinance market to strike a fair and
reasonable interest rate. Such a fair rate remains invisible at the present scenario; when the
industry is passing through a crisis. But when more and more efficient and sustainable market
players operate in the industry, the market is bound to reach its equilibrium state, identifying a
reasonable price, which remunerates the MFIs and satisfies the poor.
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APPENDIX
In 1978, Charnes, Cooper and Rhodes (CCR) formulated the CCR DEA model as a fractional
programming, which can be transformed to linear programming (LP) as follows:
Min
s. t -yi + Y.
. xi - X. 0 (1)
where X and Y are the K×N input matrix and the M×N output matrix (for the ith firm these are
represented by the vector xi and yi), respectively. is a N×1 vector of constant and is a scalar,
which stands for efficiency of ith firm. By solving this LP for each of the N firms; the efficiency
scores for each firms can be obtained. Model (1) is an input orientation DEA model under the
assumption of constant returns to scale (CRS) technology.
The CCR model assumes CRS technology and presupposes that there is no significant
relationship between the scale of operations and efficiency. But the CRS assumption is valid
only when all firms are operating at an optimal scale. Since in reality firms experience
economies or diseconomies of scale, the overall technical efficiency scores that are derived from
this model are contaminated with scale efficiencies. Considering this limitation to account the
Banker, Charnes and Cooper (BCC) model was formulated in the year 1984.
BCC model relaxed the restriction of CRS to account for variable returns to scale (VRS)
technology by adding convexity constraint to model (1). The VRS assumption provides the
measurement of pure technical efficiency (PTE), which is the measurement of technical
efficiency devoid of the scale efficiency effects. The BCC input orientation DEA model is as
follows:
Min
s. t - yi + Y.
. xi - X.
∑ = 1, 0 (2)
The first constraint states that output of the reference unit must be at least at the same level as
the output of decision-making unit 0 ( i.e. DMU 0). The second constraint states that the
efficiency corrected input usage of DMU 0 must be greater than or the same as the input use of
the reference unit. Since the correction factor is same for all types of inputs, the reduction in
observed inputs is found proportional. The third constraint ensures convexity and thus introduces
variable returns to scale.
Model (1) and model (2) can be transformed to output orientation DEA forms as shown in model
(3) and (4), respectively.
Maxø
s. t - yi + Y.
xi - X.
0 (3)
Maxø
s. t - yi + Y.
xi - X.
∑ = 1, 0 (4)
where Y, X, xi , yi and are defined as previous; denotes proportional increase in output,
which ranges from one to infinity.