technical efficiency and its determinants in ...

TECHNICAL EFFICIENCY AND ITS DETERMINANTS IN MICROFINANCE INSTITUTIONS IN INDIA: A FIRM LEVEL ANALYSIS Surender Singh et al. De Boeck Supérieur | Journal of Innovation Economics 2013/1 - n°11 pages 15 à 31

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Technical Efficiency and its Determinants IN Microfinance Institutions in India: A Firm Level Analysis Surender SINGH Department of Business Administration Chaudhary Devi Lal University, India [email protected]


Supran Kumar SHARMA School of Business Economics Shri Mata Vaishno Devi University, India [email protected]

Financial inclusion is important for improving the living conditions of the deprived sections of society including poor farmers, rural non-farm enterprises and other vulnerable groups. Financial exclusion, in terms of lack of access to credit from formal institutions, is high for small and marginal farmers and other social groups. Apart from formal banking institutions which should look at inclusion both as a business opportunity and social responsibility, the role of the self-help group movement and microfinance institutions (MFIs) is important to improve and expand the network of financial inclusion (Dev, 2006). The nature of microfinance institution is unique and quite different form traditional financial institutions like commercial banks, non-banking financial institutions, etc. MFIs are significantly smaller in size having limited resources but the key focus of their services is towards farmers, other poor households and other deprived class and often provides small collateral free group loans. MFIs pursue twin goals, i.e., financial intermediation and poverty reduction. The first goal, i.e. financial intermediaries to farmers, non-farmers and other poor households, is also known as ‘Intuitionist’s Paradigm’ (Woller et al., 1999; Murdoch, 2000). This suggests that MFIs

n° 11 – Journal of Innovation Economics – 2013/1

15


S. K. GOYAL Department of Business Management, CCS Haryana Agricultural University, India [email protected]


should generate enough revenue to meet their operating and financial costs. The second goal, known as the ‘Welfarist’s Paradigm’ focuses on poverty alleviation and depth of outreach along with achieving financial sustainability. An efficient MFI management is supposed to promote these two objectives (Brau and Woller, 2004). In the recent years microfinance has been proved as an important tool for poverty alleviation all over the world. MFIs focus on providing credit to poor who have no access to commercial banks and other financial institutions of same type and character with a vision to reduce poverty and help the poor in setting up their own income generating business. A number of reports on microfinance projects and previous researches have provided some evidences which prove that microfinance is an effective tool to help the poor get out of poverty trap (Seibel, Kunkel, 1997; Hung, 1998, etc.). Microfinance has been used in many poverty reduction projects of the government and development agencies in India. Reserve Bank of India issues guidelines to banks for mainstreaming and enhancing the outreach of micro credit providers. The organizations engaged in microfinance activities in India may be categorised as the wholesalers, non government organizations (NGOs), self help group (SHG) Federations. Despite various modes for purveying micro finance the SHG-bank linkage programme has emerged as the major micro finance programme in India. It is being implemented by commercial banks, rural regional banks and co-operative banks. The borrowing customer base of Microfinance Institutions has increased from 86.3 millions in year 2010 to 93.9 Millions in year 2011. The outstanding bank loans to MFIs were Rs. 1.26.8 billion while the MFIs have disbursed a loan of $ 1.4 billion US to 7.8 million active borrowers (www.mixmarket.org). The wholesale agencies, which provide bulk funds to the system through NGOs include the National Bank of Agriculture and Rural Development, Rashtriya Mahila Kosh, the Friends of Women’s World Banking, etc. The NGOs which are supporting the SHG Federations include MYRADA in Bangalore, Self-help Women’s Association (SEWA) in Ahmedabad, PRADAN in Tamilnadu and Bihar, ADITHI in Patna, SPARC in Mumbai, the Association for Sarva Seva Farms in Chennai, the Small Industries Development Bank of India and the Tamil Nadu Women’s Development Corporation, etc. The NGOs that are directly enhancing credit to the borrowers include SHARE in Hyderabad, ASA in Trichy, RDO Loyalam Bank in Manipur, etc. A number of attempts have been made to measure the level of efficiency of MFIs using different parametric and non parametric approaches across the world. Farrington (2000) and Lafourcade et al. (2005) have used ratio analysis technique for MFI efficiency measurement, whereas Desrochers and Lamberte (2003) used stochastic frontier analysis for the measurement of

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Journal of Innovation Economics – 2013/1 – n° 11


Surender SINGH, S. K. GOYAL and Supran Kumar SHARMA


efficiency of MFI. Both the ratio analysis and stochastic frontier analysis techniques have limitations in using multiple inputs and multiple outputs for estimating the joint efficiency of MFIs. This can be effectively prepared by Data Envelopment Analysis (DEA), a non-parametric method that does not impose a priori functional form for production technology. Qayyum and Ahmad (2006) follows up the DEA efficiency analysis with a sustainability assessment using scale parameter and ranked 25 Indian MFIs on efficiency scores. Gutiérrez-Nieto et al. (2007) used DEA analysis to assess efficiency of MFIs and suggested a methodological approach that goes behind a DEA measure and explains the scores obtained under different choices of models and specifications. The present study considers banking and social aspects of functioning of an MFI. Pal Debdatta (2010) used three years average data (2007-09) to manage the problem of missing data in the panel on 39 microfinance institutions in India using DEA technique and found only two efficient MFIs under constant returns to scale and six under variable returns to scale modeling. Haq et al. (2010) experienced that microcredit is the provision of small loans to very poor people for self-employment projects that generate income. The study while measuring the cost efficiency of 39 microfinance institutions across Africa, Asia and the Latin America used non-parametric data envelopment analysis. The study revealed that non-governmental microfinance institutions are the most efficient ones under production approach, whereas bank-microfinance institutions turned most efficient under intermediation approach. Islam et al. (2011) empirically examined the efficiency of agricultural microfinance borrowers in rice farming in Bangladesh using DEA. Inefficiency effects are modeled as a function of farm-specific and institutional variables. The results of the study revealed that subsequent to effectively correcting for sample selection bias, land fragmentation, family size, household wealth, on farm-training and off farm income share are the major determinants of inefficiency. The efficient functioning of MFIs is of paramount importance for long run sustainability, which refers to the capability of the institutions to generate enough income to at least repay the opportunity cost of all inputs as well as assets (Chaves, Gonzalez Vega, 1996). The efficient functioning or performance of any firm is adjudged with its productivity and/or efficiency. The efficiency of a firm runs down to the comparison between observed and the potential output/input. Keeping in view the above facts, the present study attempts to identify the most efficient/ best practice MFIs that would in turn help in improving the functioning of other MFIs in the country. The study also seeks to identify the factors responsible for variations in efficiency level. Identification of such factors will help the MFIs to increase their efficiency level.


17


Technical efficiency and its determinants…


The rest of the paper is structured as follows. The next section discusses the methodology used to analyze MFI efficiency. The section 3 concentrates on the results and discussion made for the study, whereas the section 4 provides a summary and finally draws the conclusion and policy recommendations from the study on the MFI efficiency.

METHODOLOGY


Data used in the study is obtained from Mix Market Network (www.mixmarket.org). In the present attempt a total of 41 microfinance institutions (out of a total 155 MFIs working in India) have been sampled depending upon the availability of data for five consecutive years 2005-2009. Out of the total selected MFIs 27 are in southern part of India. List of microfinance institutions included in the study is provided in Table 1.

Technical Efficiency-Approaches The present study employs the DEA model as it integrates multiple inputs and outputs. Furthermore, a parametric functional form does not have to be specified and DEA does not require any price information for dual cost function as is required for parametric approaches. On the same lines, the DEA has the potential to provide information to the supervisors in improving the product/efficiency of the organization. Finally, Data Envelopment Analysis presents a generalization approach because assumptions other than constant returns to scale can be accommodated within a convex piecewise linear best practice frontier. Data Envelopment Analysis has traditionally been used for the measurement of efficiency of non-profit organizations (such as hospital) and banks (Sherman, Gold, 1985). DEA terminology was first developed by Charnes et al. (1978) although the concept originated from the work of Farrell (1957). DEA involves the calculation of efficiency by comparing aggregate input/output ratios of each decision making unit with a piecewise frontier surface (representing fully efficient operation), constructed from the data set by linear programming methodology. Efficiency can be measured by (i) an input-oriented process, which focuses on reducing inputs to produce the same level of outputs and (ii) an output-oriented process which aims to maximize outputs from a given set of inputs. As mentioned earlier, the DEA method neither requires specification

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Database the Study


of a functional form for the production relationship underlying it nor makes assumptions regarding the economic behaviour of the firms. Thus, the DEA technique is ideal for analyzing the public service sector, where economic behaviour such as profit maximization may not apply. Moreover, the peer information is very useful for managerial purpose. The notion of efficiency was first proposed by Farrell (1957) which shows the ability of the firm to produce existing level of output with the minimum inputs (Input-oriented) or to produce maximum possible output with a given set of inputs (output-oriented). The measurement of technical efficiency (TE) under the assumption of constant returns to scale (CRS) is known as overall technical efficiency (OTE). Under variable returns to scale (VRS), this OTE can be further bifurcated into pure technical efficiency (PTE), i.e., managerial efficiency and scale efficiency (SE) and both are mutually exclusive and non-additive. The pure technical efficiency is obtained by estimating efficient frontier (technical efficiency) under variable returns to scale (VRS) reflecting the deviations from the frontier only due to managerial inefficiency and has been used as a managerial performance index. The scale efficiency is obtained by the ratio of overall technical efficiency (OTE) and pure technical efficiency (PTE), which shows the institution’s ability to choose the optimum scale of its operations. The scale efficiency can assume three forms, i.e., constant returns to scale, increasing returns to scale and decreasing returns to scale.

Analytical Model In the present attempt both the models of Data Envelopment Analysis – the constants returns to scale (CRS) model (Charnes, Cooper and Rhodes Model) and the variable returns to scale (VRS) model (Bankers, Charnes and Cooper Model) – under both input-oriented and output-oriented versions have been used (Charnes, Cooper, Rhodes, 1978; Bankers, Charnes, Cooper, 1984). The measurement of efficiency exclusively on the basis of any of the two approaches may be biased and disingenuous as the prime objective of MFIs is social welfare and financial inclusion. Hence, the study has applied both the models to prepare a more reliable and effective policy implications. 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 MFIs can increase their output without affecting their existing input levels. An output-oriented model (OOM) implies that the efficiency is estimated by the output of the firm relative to the best-practice level of output for a given level of inputs. In order to specify the mathematical formulation of


19




the OOM, let’s assume that there are K decision making units (DMUs) using N inputs to produce M outputs. Inputs are denoted by Xjk (j=1,…., n) and the outputs are represented by Yik (i= 1,...,m) for each MFI represented by k (k=l,…, K). The efficiency of the DMU can be measured as (Coelli et al., 2002; Worthington, 1999; Shiu, 2002) m

n

i=1

j=1

TEk = � uiyik / � vjxjk

Where, Yik is the quantity of the ith output produced by the kth DMU, Xjk is the quantity of jth input used by the kth firm, and ui and vj are the output and input weights, respectively. The DMU maximizes the efficiency ratio, TEk, subject to n

i=1

j=1

Where, ui and vj ≥ 0


The above equation indicates that efficiency measure of a firm cannot exceed one, where the input and output weights are positive. The weights are selected in such a way that the firm maximizes its own efficiency. To select optimal weights the following (Coelli et al., 2002; Worthington, 1999; Shiu, 2002) mathematical programming (output-oriented) is specified: Max: TEk Subject to: m

� uiyik − xjk + w ≤ 0

r =1,…, K

i=1

m

vjxjk − � u iXjk ≥ 0, and ui and vj ≥ 0 i=1

On the other hand, an input oriented method (IOM) is used in order to obtain the given level of output by inputs minimization. Therefore, the following mathematical programming model (input oriented) is specified (Banker, Thrall, 1992; Coelli et al., 2002; Worthington, 1999; Shiu, 2002; Topuz et al., 2005). Min: 1/ TEk m

Subject to: � uiyik − yjk + w ≥ 0 where, k = 1, …, K i=1

n

xjk − � ujxjk ≥ 0 and ui and vj ≥0 j=1

The above model shows CRS if w = 0 and it changed into variable returns to scale (VRS) if w is used unconstrained.

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m

TEk = � uiyik / � vjxjk ≤ 1


The assumption of CRS is correct only as long as the DMUs are operating at an optimal scale (Coelli et al., 2002). Although the MFIs are also financial institutions, but their approach and motive differ from other financial institutions. The various constraints on inputs like accessibility to funds may cause the unit to operate at a non-optimal scale and will cause the technical efficiency measures to be influenced by scale efficiencies and thus, making analysis of critical aspects of the study only on the basis of overall technical efficiency (OTE) may be devious, so behavior of overall technical efficiency (OTE) measurement have been studied more minutely and empirically with the help of pure technical efficiency (PTE) and scale efficiency (SE).


To make a rational selection and classification of inputs and outputs in the effective measurement model, the previous studies resort to production approach and financial intermediation approach which are commonly used for efficiency analysis among financial institutions (Berger, Humphrey, 1997). Some studies under the production approach treat loans and deposits as outputs and labour and other capital resources as inputs (Soteriou, Zenios, 1999; Vassiloglou, Giokas, 1990). On the other hand under the financial intermediation approach deposits are treated as inputs with a surplus generation as output (Berger, Mester, 1997; Athanassoupoulos, 1997) and financial institutions are considered as institutions transferring resources from savers to investors; whereas under the production approach, the financial institutions are considered as the producers of deposits and loans. The choice of inputs and output varies with the approach applied in each study. Therefore, the input choice under financial intermediation approach, tentatively, is labour, capital and materials whereas output choice is number of borrowers and number of savers. In case of intermediation approach, tentative inputs can be defined as labour, capital cost and interest payable on deposits, whereas the loans and financial investments are considered as output. The loans or credit offers without any collateral requirements is the most important financial services that MFIs provide to their customers. Therefore, the present attempt chooses Gross Loan Portfolio as a single output under financial intermediation approach (Sealey, Lindley 1977; Berger et al., 1993, Elyasiani, Mehdian 1990; Casu, Molyneux 2000; Isik, Hassan 2003). The prime inputs required to produce loans are use of labour and amount of expenditure made (Norman, Stocker, 1991). In this way, in the present attempt number of personnel as a proxy for labour and cost per borrower as a proxy for expenditure are taken as two inputs.


21


Approaches for Efficiency Analysis of Financial Institutions


Model for Factors Affecting Efficiency of MFIs


Y= f (TA, AGE, DER, NAB, ROA, Location, OSS, BPS) Y = Individual technical efficiency measures of MFI, i.e., TE, PTE and SE TA= Total assets (amount in US $) AGE = Age of the MFI measured as the number of years of operation DER = Debt- equity ratio NAB = Total number of active borrowers ROA = Return on asset (in percentage) Location = location dummy =1, if MFI is located in South India, otherwise =0 OSS = Operational self sufficiency (in percentage) BPS = Borrowers per staff The variables included in the regression model can have important implications on the operations and efficiency of MFIs in India. The total assets and age of the firm are included in the model to access the effects of size and experience of MFI on their efficiency level. The coefficients of both of these variables are expected to be positive. It is expected that large firms having more resources at their disposal may perform better than smaller size firms. Similarly, the older firms with more experience can utilize their resources more efficiently as compared to new firms. The coefficient of variable debtequity ratio, which represents the financial management of MFIs, is expected to be negative. It is expected that higher debt-equity ratio reduces firm’s efficiency as it reflects the higher financial dependence of the MFIs on borrowed money. Further, to test the expected trade off between efficiency and

22



The study also investigates the possible determinants of efficiency of MFIs in India. On the basis of previous studies and experiences, different variables are included that can explain the level of efficiency of MFIs. These variables can be divided into different groups based on basic characteristics, governance, financial management, performance and location. Identifications of such factors will help the new and existing MFI to increase their efficiency level (Elyasiani, Mehdian 1990; Casu, Molyneux 2000; Isik, Hassan 2003; Masood, Ahmad, 2010). The present attempt resorts to both the correlation and the regression analysis for finding out the efficacy of these variables. Since the level of efficiency (OTE, PTE, SE) lies between 0 and 1 and considered as limited dependent variable. Thus, in the present format a Tobit regression model is specified to find out the major determinants of individual technical efficiency measures of MFIs.


outreach, the information on number of active borrowers (NAB) is included in the model. Location is the qualitative variable, MFIs in India are concentrated in the South Indian states and it is reflected from the earlier studies that these MFIs work more efficiently than those located in other parts of the country (Massod, Ahmad, 2010). Hence, a dummy variable is included to identify the present status for such purpose. On the same lines, to know the overall performance of the MFIs two variables namely operational self sufficiency (OSS) and rate of return on assets (ROA) are included in the model. The operational self sufficiency (OSS) represents the ability of MFIs to meet their operating costs from their income. It indicates whether enough revenue is earned to cover the organizations’ costs which included financial expenses, operating expense as well as impairment loss. The operational self sufficiency (OSS) represents the financial ability of MFIs that may lead to efficiency of MFIs. At the same time, return on assets (ROA) is expected to have positive association with firm efficiency. As foremost consideration of an MFI is the livelihood promotion by meeting the unmet credit demand of economically challenged section of the society, borrowers per staff (BPS) have been considered as a proxy for the coverage by an MFI within the given set of resources to meet the credit needs of the target group.

RESULTS AND DISCUSSION Technical Efficiency of MFIs The empirical estimates of technical efficiency and its components of MFIs in India are reported in the Table 1. The results reveal that two MFls, i.e., Sanghamithra and Spandana are on the efficiency frontier, i.e., technically efficient (Technical efficiency=1) when constant returns to scale is operational in the model. Whereas, AWS, Nidan, SKS, Sanghamithra and Spandana turn out to be most efficient microfinance firms (Pure Technical efficiency=1) under variable returns to scale. Thus, MFls which remain efficient under both constant returns to scale (CRS) and variable returns to scale (VRS) assumptions are Sanghamithra and Spandana. The mean overall technical efficiency (OTE), pure technical efficiency (PTE) and scale efficiency (SE) using input-oriented approach turns out to be 40.6 per cent, 56.8 per cent and 71.6 per cent, respectively. On the other hand, the respective efficiency scores are estimated to be 40.6 per cent, 49.0 per cent and 84.7 per cent using output-oriented measures. The estimates on technical efficiency suggest that under input oriented measure, 43.2 per cent of inputs can be decreased without affecting the existing output level, i.e.,


23




gross loan portfolio of MFls. Whereas, under the output oriented measures the MFls can increase their loan portfolio by 51 per cent with the existing level of inputs by efficient utilization of these inputs. The overall technical efficiency of the MFIs under input oriented approach as well as output oriented approach turns out to be 40.6 per cent thereby indicating that input can be reduced by 59.6 per cent for the same level of output or output can be increased by 59.6 per cent with the same level of inputs. The Table 1, further, reveals that the pure technical efficiency is greater than the overall technical efficiency in about 75 per cent of the total MFIs studied under input oriented cases and in more than 90 per cent MFIs under output oriented cases. It implies that most of the times technical inefficiency of MFls is due to the scale inefficiency rather than the pure technical inefficiency (i.e., managerial inefficiency). Furthermore, as per Table 1 a total of 61 per cent (25 out of 41) of MFIs are found realizing increasing returns to scale (IRS) under input oriented measure; whereas only 24 per cent (10 out of 41) of MFIs studied experienced IRS under output oriented measure. Table 1 – DEA efficiency of MFIs in India MFIs

Input oriented

Output oriented

TE

PTE

TE

PTE

SE

Adhikar

0.233

0.456 0.512

AML

0.893

0.957 0.932

Irs*

0.233

0.285

0.818

Drs**

drs

0.893

0.959

0.931

Asomi

0.201

0.451 0.446

drs

irs

0.201

0.221

0.909

AWS

0.879

drs

0.879

irs

0.879

1

0.879

Bandhan

irs

0.556

0.575 0.967

irs

0.556

0.559

0.995

drs

BASIX

0.359

0.401 0.894

drs

0.359

0.415

0.865

drs

BFL

0.557

0.604 0.922

drs

0.557

0.66

0.843

drs

BISWA

0.222

0.487 0.456

irs

0.222

0.227

0.976

irs

BSS

0.451

0.513 0.879

drs

0.451

0.6

0.752

drs

Cashpor MC

0.321

0.345

drs

0.321

0.374

0.856

drs

CReSA

0.294

0.519 0.567

irs

0.294

0.306

0.964

drs

ESAF

0.242

0.253 0.957

drs

0.242

0.305

0.793

drs

GFSPL

0.438

0.598 0.733

drs

0.438

0.632

0.692

drs

Grama Vidiyal

0.461

0.515 0.894

drs

0.461

0.539

0.855

drs

GU

0.337

0.492 0.685

irs

0.337

0.413

0.815

drs

Janodaya

0.252

0.571

irs

0.252

0.263

0.955

drs

JFSL

0.158

0.379 0.416

irs

0.158

0.158

0.995

drs

KBSLAB

0.412

0.981

drs

0.412

0.535

0.77

drs

MMFL

0.348

0.575 0.605

irs

0.348

0.36

0.967

irs

NBJK

0.193

0.914 0.211

irs

0.193

0.215

0.899

irs

24

1

0.44 0.42

SE

0.93

Returns to Scale

Returns to Scale





Input oriented TE

PTE

SE

Returns to Scale

TE

PTE

SE

Returns to Scale

Nidan

0.032

0.032

irs

0.032

1

0.032

irs

PWMACS

0.461

0.625 0.738

irs

0.461

0.477

0.967

irs

RASS

0.776

0.904 0.858

irs

0.776

0.78

0.995

irs

RGVN

0.244

0.378 0.646

irs

0.244

0.314

0.778

drs

Saadhana

0.419

0.426 0.982

irs

0.419

0.52

0.806

drs

-

1

1

1

-

Sanghamithra

1

1

Output oriented

1

1


Sarvodaya Nano Finance

0.287

0.507 0.565

irs

0.287

0.293

0.977

irs

SCNL

0.286

0.336 0.852

drs

0.286

0.411

0.696

drs

SEWA Bank

0.423

0.436 0.972

irs

0.423

0.513

0.825

drs

SHARE

0.804

0.832 0.966

drs

0.804

0.835

0.963

drs

SKDRDP

0.606

0.629 0.962

irs

0.606

0.61

0.992

drs

SKS

0.644

0.644

drs

0.644

1

0.644

drs

0.585 0.615

irs

0.36

0.363

0.991

irs

-

1

1

1

-

SMSS Spandana

0.36 1

1 1

1

Sonata

0.143

0.194 0.734

irs

0.143

0.199

0.716

drs

SU

0.185

0.477 0.387

irs

0.185

0.222

0.831

drs

Swadhaar

0.09

0.388 0.233

irs

0.09

0.098

0.922

drs

SWAWS

0.416

0.438 0.949

drs

0.416

0.546

0.763

drs

Ujjivan

0.232

0.317 0.731

drs

0.232

0.355

0.653

drs

VFS

0.333

0.373 0.891

irs

0.333

0.429

0.774

drs

WSE

0.089

0.558 0.159

irs

0.089

0.103

0.864

irs

Mean

0.406

0.568 0.716

0.406

0.49

0.847

Note: * and ** refers to increasing returns to scale and decreasing returns to scale, respectively.

Frequency Distribution The frequency distribution of efficiency scores of sampled MFIs under both the input and output oriented measures is presented in Table 2. The results indicate wide variations in efficiency level of sample microfinance institutions. In terms of technical efficiency majority of the sample units i.e. 76 per cent of MFIs have efficiency scores below 51 per cent, whereas only 4.88 per cent firms have technical efficiency level above 90 per cent under input oriented as well as output oriented approaches. The corresponding figures are 49 per cent and 19 per cent under pure technical efficiency (PTE) and 20 per cent and 13 per cent under scale efficiency (SE) for input-oriented model, respectively. In terms of pure technical efficiency (PTE) slightly less than half of the sampled MFIs (49 per cent) under input oriented measure and 56 per cent under output measures have efficiency less than or equal to 50 per cent. About 22 per cent of MFIs have efficiency between 51 to 60 per


25


MFIs


cent under input oriented measure whereas this percentage was about 15 per cent under output measures. The percentage of units having efficiency above 90 per cent was 20 and 15 per cent under input and output-oriented measures in terms of pure technical efficiency (PTE), respectively. In terms of scale efficiency (SE) under both the measures, the highest percentage of sampled MFIs falls in the range of more than 90 per cent efficiency. Under input-oriented measure about 40 per cent of MFIs have efficiency more than 80 per cent whereas this percentage was 70 under output oriented measures in terms of scale efficiency. Table 2 – Frequency distribution of efficiency of MFIs in India Input oriented

Output oriented


TE

PTE

SE

TE

PTE

SE

31 (75.61)

20 (48.78)

8 (19.51)

31 (75.61)

23 (56.09)

1 (2.44)

51-60

2 (4.88)

9 (21.95)

4 (9.76)

2 (4.88)

6 (14.63)

0

61-70

2 (4.88)

3 (7.32)

5 (12.19)

2 (4.88)

4 (9.76)

4 (9.76)

71-80

1 (2.44)

0

4 (9.76)

1 (2.44)

1 (2.44)

7 (17.07)

81-90

3 (7.32)

1 (2.44)

7 (17.07)

3 (7.32)

1 (2.44)

12 (29.27)

90-100

2 (4.88)

8 (19.51)

13 (21.71)

2 (4.88)

6 (14.63)

17 (41.46)

Minimum

0.03

0.19

.03

0.03

0.10

.03

Maximum

1.00

1.00

1.00

1.00

1.00

1.00

Mean

0.4063

0.5682

0.7159

0.4063

0.4901

0.8468

SD (%)

24.78

23.04

26.15

24.78

27.00

16.75

41

41

41

41

41

41

≤50

Total

Note: Figures in parentheses indicate percentage to total number of sample units

Therefore, there is a huge potential of increasing efficiency of MFIs by decreasing inputs use without effecting the existing output level under input-oriented measures whereas under output oriented measures by increasing output with the existing level of inputs by efficient utilization of inputs.

Factors Affecting Efficiency of Microfinance Institutions in India Correlation coefficients among the three different efficiency measures and explanatory variables defined have been calculated and the results are

26



Efficiency (%)


p resented in Table 3. The results show that correlation coefficient of value of total assets is positive with all the efficiency measures and that of age is positive with pure technical efficiency (PTE) and scale efficiency (SE). The dummy variable used for location exhibits positive correlation with efficiency measures. It indicates that MFIs from southern India have positive correlation with all the three measures of efficiency. However, debt equity ratio is negatively related to pure technical efficiency and scale efficiency measures as expected. Similarly, return on assets (ROA) and operational self sufficiency (OSS) which represents the financial ability of MFIs have positive correlation with all the measures of efficiency.


Variables

TE

PTE

SE 0.282

Log(TA)

0.580

0.405

Age

-0.008

-0.031

0.048

DER

-0.144

-0.206

-0.157

NAB

0.528

0.520

0.092

ROA

0.208

0.201

0.021

Location

0.510

0.404

0.208

OSS

0.312

0.242

0.182

BPS

0.169

0.464

0.290

The results of qualitative response model (Tobit regression) measured to identify the factors influencing efficiency are worked out and presented in Table 4. The regression estimates for output-oriented model of technical efficiency as dependent variable show expected sign for all except age and return on assets (ROA). Thus, association of age of the firm and the level of efficiency suggest that new firm can also achieve higher level of technical efficiency. Hence, it is not the experience but strong fundamentals, rational policy and management that pave the way for higher efficiency. The coefficient of debt-equity ratio (DER) is experienced to be negative which reflects that higher debt-equity ratio reduces the efficiency level of MFIs in India The coefficients of the variables comprise of assets and borrowers per staff (BPS) are positive and statistically significant at 5 per cent level of significance reflecting that in the microfinance sector it is the level of efforts and results on ground level that matters and not the higher number of staff members. The coefficient of location turned out to be statistically significant and positive with dummy variable assigned value 1 to south Indian MFIs, which indicates that the technical efficiency of MFI in south India is higher as


27


Table 3 – Correlation coefficients between difference technical efficiency measures and variables defined


compared to north India. For few of the similar variables used in the model quite identical results have been estimated by Massod and Ahmad (2010).


Variable

OTE

PTE

SE

Intercept

0.009 (0.1808)

0.701* (0.253)

0.701 (0.253)

TA

4.64E-09** (2.16E-09)

4.35E-09 (3.00E-09)

4.35E-11 (3.00E-07)

Age

-0.0041 (0.003)

-0.0071 (0.0047)

0.0078 (0.0047)

DER

-0.0001 (0.0007)

-0.0016 (0.0009)

-0.0016*** (0.0009)

NAB

-5.68E-07 (3.622E-07)

-3.95E-07* 5.06E-07)

3.95E-07 (5.07E-07)

ROA

-0.175 (0.331)

0.938 (0.603)

0.938* (0.603)

0.0893*** (0.0567)

0.034 (0.0779)

0.034 (0.077)

OSS

0.107 (0.129)

0.269 (0.185)

0.269* (0.185)

BPS

0.0008* (0.0001)

0.0006* (0.0002)

0.0006 (0.0002)

Pseudo R2

3.363

1.108

1.138

LR Chi-Square

48.64

35.34

18.09

Log Likelihood

17.091

11.66

16.99

Location

*, **, and *** indicate coefficient is significant at 1, 5 and 10 per cent level, respectively.

Taking pure technical efficiency (PTE) as dependent variable, the coefficient of borrowers per staff (BPS) is found to be statistically significant at 5 per cent level of significance. In the regression estimates for scale efficiency (SE) as dependent variable, the coefficients of three variables namely debt-equity ratio (DER), return on assets ROA) and operational self sufficiency (OSS) are found to be statistically significant. The null Hypothesis H0: d1 = d2 = … = d8 = 0 specifies that the coefficients of all the variables taken together are zero. The hypothesis is rejected using LR Chi-Square statistics in all the three cases taking technical efficiency (TE), pure technical efficiency (PTE) and Scale efficiency (SE) as dependent variables. The Pseudo R2 and Log Likelihood are also at satisfactory level in all the cases. The rejection of hypothesis suggests that although individual coefficients of some variables included in the model are not significant, but jointly they are explaining variations in efficiency of MFIs.

28



Table 4 – Determinants of efficiency of MFIs in India



The study uses DEA for estimating efficiency of 41 MFIs in India using input-oriented and output-oriented approaches. The findings pinpoint that there are two efficient MFIs under CRS and other three more MFIs, i.e., AWS, Nidan and SKS are found efficient under variable returns to scale (VRS) only. The findings suggest that output of MFIs can be increased by 59.4 percent without increasing the quantum of inputs. In other words, the study highlights that same level of gross loan portfolio can be obtained by reducing the inputs (i.e. Number of personnel as a proxy for labour and cost per borrower) by 59.4 per cent. Hence, there is considerable scope to enhance the output or to reduce inputs in MFIs in India. The study found 25 MFIs realizing economies of scale under input oriented measure; whereas only 10 MFIs under output oriented measure. These MFIs are found to have potential to expand widely as returns to scale is found favourable to them, so there is urgent need to put attention towards the rational expansion of these MFIs as the first priority. The findings show wide regional variations in efficiency scores of the sampled MFIs as the MFIs operating in the south India are found more efficient. The regression estimates show expected sign for all variables except age and return on assets (ROA). However, coefficient of age and return on assets (ROA) are not statistically significant. The policy implication of the study establishes that new firms can also achieve higher level of efficiency with strong fundamentals, rational policy and management. The MFIs should concentrate on their efforts on ground level by increasing the customer base rather than higher staffing that matters in achieving higher efficiency. There is a huge scope for MFIs to increase their operations in north India as there is potential to raise the level of efficiency of their operations as compare to southern part of India. While, concluding it is pertinent to mention that in the present study, the average data of five consecutive years have been used to measures the technical efficiency of MFIs, there is further scope for research on comparing the technical efficiency and using Malmquist productivity index measuring the level of productivity of individual MFIs on a larger panel data set which would show the direction and minute behaviour of technical efficiency and productivity of MFIs in the Indian context. The non-availability of the borrower’s side secondary data relating to the utilisation of the borrowed funds, microeconomic activities undertaken, average duration of loans and loan installments may have limited the accuracy of efficiency measurement of the model. Further, more minute and empirical analysis can be prepared by sub-dividing the MFIs on the basis of their legal status as a society/trust/ companies, cooperative/credit union, etc. to study the variation in technical efficiency across the different types and states of Indian Union.


29


CONCLUSIONS AND SUGGESTIONS


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NORMAN, M., STOCKER, B. (1991), Data Envelopment Analysis: The Assessment of Performance, John Wiley & Sons (eds), New York, 9-16.

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