Probing on the Efficiency and Sustainability Status of Indian Microfinancing Institutions

PROBING ON THE EFFICIENCY AND SUSTAINABILITY STATUS OF INDIAN MICROFINANCING INSTITUTIONS: A DATA ENVELOPMENT ANALYSIS APPLICATION

Nadiya M[1] and T Radha Ramanan[2]

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 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 sector[3], 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 attention[4]. 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 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 thesample 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 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 6draws 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 Specification / Findings
Ngheim, Coelli & Rao (2006) / 46 MFIs in Vietnam / Inputs: Labour cost and Non-Labour costs
Outputs: Number of savers, Number of borrowers
and Number of groups / MFIs are found technically efficient, with an average technical efficiency score of 80 percent. Age & Location of MFI are found to have a significant influence upon efficiency scores.
Gutierrez-Nieto, Cinca & Molinero (2006) / 30 MFIs in Latin America / Inputs: Number of credit officers and Operating expenses
Outputs: Number of loans outstanding, Gross loan portfolio and Interest and fee income / Using multivariate analysis, efficiency is found to be affected by country effect and status of MFI.
Qayyum & Ahmad (2006) / 85 MFIs in South Asia ( 15 Pakistani, 25 Indian and 45 Bangladeshi MFIs) / Inputs: Credit officers and Cost per borrower
Output: Loans disbursed by MFI / The study attributes inefficiencies in the three South Asian regions to be technical in nature, which calls for more managerial and technological improvements.
Sufian(2006) / 20 MFIs in Malaysia / Inputs: Total Deposits and Fixed Assets
Outputs: Total Loans and Other Income / The study observed only 28.75 percent of all Malaysian MFIs to be efficient and more profitable. Size and market share are found to have a negative effect on efficiency.
Bassem(2008) / 35 MFIs in the Mediterranean / Inputs: Personnel and Total Assets
Outputs: Number of women borrowers and Return on Assets / Eight MFIs in the region are found technically efficient. Size of MFI is found to have a negative effect on efficiency.
Haq, Skully & Pathan (2009) / 39 MFIs across Africa, Asia & Latin America / Inputs: Labour, Cost per borrower
and Cost per saver
Outputs: Savers per staff member and borrowers per
Staff member. / Results showed non-governmental MFIs to be efficient under production approach and bank-MFIs to be efficient under 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 institutionsare 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 study[6].

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 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

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 analysisand 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)