efficiency of microfinance institutions: a data envelopment analysis

Asia-Pacific Finan Markets (2010) 17:63–97DOI 10.1007/s10690-009-9103-7

Efficiency of Microfinance Institutions: A DataEnvelopment Analysis

Mamiza Haq · Michael Skully · Shams Pathan

Published online: 17 October 2009© Springer Science+Business Media, LLC. 2009

Abstract This study examines the cost efficiency of 39 microfinance institutionsacross Africa, Asia and the Latin America using non-parametric data envelopmentanalysis. Our findings show non-governmental microfinance institutions particularly;under production approach, are the most efficient and this result is consistent withtheir fulfillment of dual objectives: alleviating poverty and simultaneously achiev-ing financial sustainability. However, bank-microfinance institutions also outperformin the measure of efficiency under intermediation approach. This result reflects thatbanks are the financial intermediaries and have access to local capital market. It maybe possible that bank-microfinance institutions may outperform the non-governmentalmicrofinance institutions in the long run.

M. Haq (B)University of Queensland Business School, The University of Queensland, St. Lucia,QLD 4072, Australiae-mail: [email protected]

M. HaqRMIT University, Victoria, Australiae-mail: [email protected]

M. SkullyDepartment of Accounting and Finance, Monash University, PO Box 197, Caulfield East,Victoria 3145, Australiae-mail: [email protected]

S. PathanFaculty of Business, Technologyand Sustainable Development, Bond University, Robina, Gold Coast,QLD 4229, Australiae-mail: [email protected]

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Keywords Africa · Asia · Data envelopment analysis · Efficiency · LatinAmerica · Micro finance institutions

1 Introduction

Microfinance institutions (MFIs) provide a range of financial services to poor house-holds.1 Their worldwide growth in numbers has had a positive impact by providingthe poor with loans, savings products, fund transfers and insurance facilities. This hashelped create an encouraging socio-economic environment for many of these devel-oping countries households.

The nature of these institutions is quite different from traditional financial institu-tions (such as commercial banks). MFIs are significantly smaller in size, limit theirservices towards poor households and often provide small collateral-free group loans.Most MFIs depend on donor funds and are not-for-profit oriented organizations thatshare a common bond among the members. They also differ in their two main oper-ational objectives. First, as mentioned they act as financial intermediaries to poorhouseholds. This is known as the ‘institutionist paradigm’ (Woller et al. 1999; Murdoch2000) which affirms that MFIs should generate enough revenue to meet their operatingand financing costs. Second, they have a social goal. This can be defined as the ‘welf-arists’ paradigm’ which includes a focus on poverty alleviation and depth of outreachalong with achieving financial sustainability.2 An efficient MFI management shouldpromote these two objectives (Brau and Woller 2004).

The formal MFI institutions (bank MFIs, non bank financial institution MFIsand cooperative MFIs) are subject to prudential regulation and their activities li-censed; mainly delivering credit facilities to their members. Some of these also mobi-lize savings from non-members. In contrast, semiformal MFI institutions, typicallynon-government organization MFIs (NGO-MFIs), are usually unregulated but regis-tered under some society legislation. Table 1 shows the range of products and fundingsources each type entail. Finally, the informal MFI institutions include money lend-ers, shop keepers and pawn brokers. Unfortunately, their small size and often lack oflicensing make them difficult to identify and so they are excluded from our study. Thequestion, though, among the remaining four MFI types is whether one category mayprove more efficient than the others.

MFIs with the largest asset size are found in Asia. Asia also has the most efficientMFIs due to large population densities and lower wages (Microbanking Bulletin 2004).Other factors such as strong outreach and preservation of low operating expenses havealso helped Asian MFIs to be efficient. However, South Asian MFIs are relatively moreefficient than their counterparts in East Asian MFIs.3 This differences in efficiency

1 Poor households are defined as under the international poverty line of 1.00US$ per day. Maxwell (1999)reports the poor households generally fall under the category of income/consumption poverty, social exclu-sion, lack of capability and functioning, vulnerability, livelihood unsustainability and relative deprivation.2 Welfarists’ paradigm believes MFIs can achieve sustainability without being self-sufficient. They suggestdonor fund is the equity and donors are the social investors.3 South Asian MFIs incur on an average 25 dollars per borrower while Philippines, Vietnam, Indonesiaand Cambodia incur double the cost.

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Efficiency of Microfinance Institutions: A Data Envelopment Analysis 65

Table 1 Features of micro finance institutions across Africa, Asia and the Latin America

Microfinanceinstitutions

Type, statusand reg.

Productsoffered

Percentage ofMF

Main fundingsource

Ruhunadevelopmentbank/SouthAsia/SriLanka

Formal/bank/reg. L, VS, L, LE,FT, T&C

81–90% Shareholdercapital,savings,loans andgrants

NirdhanUtthan bankLtd./SouthAsia/Nepal

Formal/bank/reg. L, VS, I 91–100% Grants, loans,savings,shareholdercapital

First Microfi-nance BankLtd./SouthAsia/Pakistan

Formal/bank/reg. L, VS, I, FT 91–100% Shareholdercapital,grants,savings

K-REP/Africa/Kenya

Formal/bank/reg. L, VS, I, FT 91–100% Savings loans,share holdercapital

Bank Solidario/Latin America/Ecuador

Formal/bank/reg. L, VS, FT 91–100% Loans, savingsand shareholdercapital

Centenary Rural DevelopmentbankLtd/(CERUDEB)/Africa/Uganda

Formal/bank/reg. L, VS, FT 91–100% Savings

Amhara Credit and SavingsInstitutions(ACSI)/Africa/Eithiopia

Formal/bank/reg. L, VS, FT 91–100% Grants andloans

Association ofCambodianLocal EconomicDevelopmentAgencies(ACLEDA)/EastAsia/Cambodia

Formal/bank/reg. L, VS, FT,T&C

91–100% Savings andshareholdercapital

Banco Sol/LatinAmerica/Bolivia

Formal/bank/reg. L, VS, FT,T&C

91–100% Loans, savingsand shareholdercapital

ChhimekBikas BankLtd(CBB)/SouthAsia/Nepal

Formal/bank/reg. L, VS, I, FT 91–100% Loans, savingsand shareholdercapital

Bank RakyatIndonesia(BRI)/EastAsia/Indonesia

Formal/bank L, VS, FT,T&C

100% Savings

GrameenBank/SouthAsia/Bangladesh

Formal/bank/reg. L, VS, I, 91–100% Grants, loans,savings andshareholdercapital

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Table 1 Continued



Productsoffered

Percentage ofMF

Main fundingsource

Dedebit Credit and Savingsinstitutions(DECSI)/Africa/Eithiopia

Formal/bank/reg. L, VS, I, FT 91–100% Savings

Equity Bank(EBS)/Africa/Kenya

Formal/NBFI/reg. L, VS, FT,T&C

91–100% Loans, savingsandshareholdercapital

Angkor Mikro-heranhvathoKampuchea(AMK)/EastAsia/Cambodia

Formal/NBFI/reg. L, VS 91–100% Shareholdercapital

Caja MunicipalAhorro yCredito –Arequipa(CMAC)/LatinAmerica/Peru

Formal/NBFI/reg. L,VS 91–100% Savings

Commercial MicrofinanceLimited (CMF)/Africa/Uganda

Formal/NBFI/reg. L, VS, FT 91–100% Savings

Caja Nor/LatinAmerica/Peru

Formal/NBFI/reg. L, VS, I, FT 91–100% Loans,savings,shareholdercapital

Sarvodaya Economic EnterpriseDevelopment Services(SEEDS)/South Asia/SriLanka

Formal/NBFI/reg. L, VS, I, 81–90% Grants,savings andloans

ServiciosFinancierosComunitarios(FINCOMUN)/LatinAmerica/Mexico

Formal/NBFI/reg. L, VS, 90% Savings andshareholdercapital

Africa Village Financial Services(AVFS)/Africa/Ethiopia

Formal/NBFI/reg. L, VS, 91–100% Grants,savings,loans

VYCCUSavings andCreditCooperativeSocietyLtd/SouthAsia/Nepal

Formal/coop/reg. L, VS, I, T&C 71–80% Shareholdercapital,savings,loans

Cooperative deAhorro y CreditoJardinAzuayo(COAC)/LatinAmerica/Ecuador

Semiformal/coop/unreg

L, VS,FT

91–100% Loans,savings,shareholdercapital

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Table 1 Continued



Productsoffered

Percentage ofMF

Main fundingsource

Credit Mutuel du Senegal(CMS)/Africa/Senegal

Formal/coop/reg. L, VS 90% Shareholder capital,savings, loans, grants

AssociationCooperativade Ahorro yCreditoVincentina deR.L. (ACCOVI)/Latin America/El Salvador

Formal/coop/reg. L, VS, I 91–100% Loans, savings

Alliance de Credit et d’ Epargnepour la Production(ACEP)/Africa/Senegal

Formal/coop/reg. L, VS, FT 91–100% Loans

KapalongCooperative(KC)/EastAsia/Philippines

Semiformal/coop/unreg.

L, VS 91–100% Savings

NeighborhoodSocietyService Centre(NSSC)/SouthAsia/Nepal

Formal/NGO/reg. L, VS, I 81–90% Loans andsavings

Capital AidFund forEmploymentof the Poor(CEP)/EastAsia/Vietnam

Semiformal/NGO/unreg.

L, VS 91–100% Grants, loans,shareholdercapital andsavings

TYMFUND/EastAsia/Vietnam


L, VS, I, T&C 91–100% Grants, loansand savings

Bangladesh Rural AdvancementCommittee (BRAC)/SouthAsia/Bangladesh

Formal/NGO/reg.

L, VS, T&C 90% Savings,grants loans

Association for SocialAdvancement (ASA)/SouthAsia/Bangladesh


L, VS, I 91–100% Loans andsavings

Cebu Micro enterpriseDevelopmentFoundationInc(CMEDFI)/EastAsia/Philippines


L, VS, I 91–100% Grants, loans,savings,shareholdercapital

All CeylonCommunityDevelopmentCouncil(ACCDC)/SouthAsia/Sri Lanka


L, I, T & C 71–80% Shareholdercapital

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Table 1 Continued



Productsoffered

Percentage ofMF

Main fundingsource

Women DevelopmentFederation Hambantota(WDFH)/South Asia/SriLanka


L, VS, I, T &C

91–100% Savings, grants, loans,shareholder-capital

Kashf Foundation/SouthAsia/Pakistan

Formal/NGO/reg.

L, VS, I, T &C

91–100% Grants, loans

Women and Associationsfor Gain both Economicand Social(WAGES)/Africa/Togo


L, VS, T & C 91–100% Grants, loans,savings

Women DevelopmentFederation Wilgamuwa(Wilgamuwa)/SouthAsia/Sri Lanka


L, VS 81–90% Grants,savings ,shareholdercapital

Credit du Sahel(CDS)/Africa/Cameroon

Formal/other/reg.

L, VS, LE, I,FT and T &C

71–80% Savings(80%),grants,loans,shareholdercapital

This table presents the different characteristics of the MFIs. We choose those banks that provide more than50% of microfinance services to the poor households. We first describe the features of bank-MFIs followed byNBFI-MFIs, co-op-MFIs and NGO-MFIs. We have also divided these microfinance institutions into formaland semiformal institutions. Reg. represents regulated organisation while unreg. represents unregulatedorganization. The formal institutions are licensed and regulated under the prudential banking regulation andsupervision while the semiformal institutions are unregulated but registered under some society legislation.L loans, VS voluntary savings, LE leasing, I insurance, FT fund transfer, T&C training and consulting, MFmicrofinance. Source www.mixmarket.org/en/demand (accessed in Jan 2006)

may be the result of various lending methodology applied by the Asian MFIs. ManyIndian MFIs, for example, reduce their staffing costs by lending to self-help groupsrather than to the individual borrower. Our findings show that bank-MFIs are the mostefficient under intermediation approach while NGO-MFIs are the most efficient underproduction approach.

Our study chose to apply the DEA model for several reasons. First, the DEA modelis able to incorporate multiple inputs and outputs easily. Thus, DEA is particularlywell-suited for efficiency analysis of MFIs as it considers multiple inputs and producesmultiple outputs such as alleviating poverty and achieving sustainability. Second aparametric functional form does not have to be specified for the production function.Third, DEA does not require any price information for dual cost function as is requiredfor parametric approaches.4 Fourth, DEA has the potential to provide information tothe supervisors in improving the productive efficiency of the organization. Finally,DEA presents a generalization approach because other assumptions than constantreturn to scale can be accommodated within a convex piecewise linear best practice

4 Unfortunately due to lack of price information we do not apply the parametric approaches in this study.

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frontier. DEA has traditionally been used for the study of non-profit organization (suchas hospital) efficiency and bank efficiency (Sherman and Gold 1985).

The rest of the paper is structured as follows. The next section covers a brief litera-ture on efficiency measurement of MFIs. Section 3 discusses the methodology used toanalyze MFI efficiency. Section 4 presents the results. Section 5 provides a summaryand finally draws the conclusion on the MFI efficiency across the regions.

2 Literature Review

There has been little research conducted on the efficiency of microfinance institu-tion. However, several studies examine various measures of MFI efficiency acrossthe regions. Study by Farrington (2000) identifies a number of accounting variablesto reflect the efficiency of the microfinance institutions. These accounting variablesare administrative expense ratio, number of loans per loan officer and loan officers tototal staff, portfolio size, loan size, lending methodology, source of funds and salarystructure as the efficiency drivers and hence as the measurements for MFI efficiency.Lafourcade et al. (2005) use cost per borrower and cost per saver as measure of effi-ciency. They found African MFIs incur highest costs per borrower but have the lowestcosts per saver. They also mention that regulated MFIs maintain higher efficiencythrough low costs per borrower and per saver. In contrast, African cooperative-MFIsare the least efficient with the highest cost per borrower. Nevertheless, cooperative-MFIs have the lowest cost per saver but unregulated MFIs have the highest. Noneof these two studies use any parametric or non-parametric approach to evaluate theefficiency of MFIs.5

Until recently, the issue of efficiency has been less commonly examined with para-metric models. Guitierrez-Nieto et al. (2006) applied data envelopment analysis (DEA)to measure the efficiency of 30 Latin American MFIs and then explored the multivar-iate analysis of the DEA results. They developed 21 specifications using two inputsand three outputs. They identified an NGO (W-Popayan), and a non-bank financialinstitution (Findesa) as the most efficient among the group of 18 MFIs. The otherkey MFI paper is that of Hassan and Tufte (2001) using a parametric approach (sto-chastic frontier analysis or SFA) found that Grameen Bank’s branches staffed by thefemale employees operated more efficiently than their counterparts staffed by themale employees. Further, Desrochers and Lamberte (2003) have also used paramet-ric approaches to study the efficiency of cooperative rural banks in the Philippines.They found that cooperative rural bank with good governance were more efficientthan their counters laced by bad governance. Leon (2001) reported that productivityof resources, governance, and business environment were the contributing factors forthe cost-efficiency of the Peruvian municipal banks.

We add to the literature by applying DEA on MFIs where profit maximization maynot be the vital interest to policymakers and regulators. Theoretically, productivity canbe measured in terms of borrowers per staff member and savers per staff member. Thus,

5 Parametric approach includes stochastic frontier approach, flexible profit function etc and non-parametricapproach includes data envelopment analysis.

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high level of MFIs efficiency here may be a result of maintaining high productivityper employee level (Microbanking Bulletin 2005). Baumann (2005) has also foundthe relation between MFI efficiency and productivity. Lafourcade et al. 2005 showthat African MFI-staffs are highly productive as the borrowers and savers numbersper staff member are highest. This high productivity may reflect their extensive use ofgroup lending as a means of achieving economies of scale.

The Grameen Bank in Bangladesh provides credit to group-borrowers and suchlending mechanism may help to increase MFI staff productivity. It has been claimed(Microbanking Bulletin 2004) that the Latin American MFIs were relatively moreefficient than all regions except Asia. This is true in case of the Latin American coop-eratives while their NGOs were the least efficient (for example, COAC, CMAC andWWB) which reflects economies of scale as increased loan size reduces their opera-tional expenses.

We follow the work of McCarty and Yaisawarng (1993) and apply a two stageanalysis where the MFIs have controllable and uncontrollable inputs. To our knowl-edge this is first such study that applies this two stage method on MFIs. This is thefirst study to measure the efficiency of MFIs using DEA on an international basisand so is important for several reasons. Firstly, individual MFI performance is mea-sured internationally by MFI type. This may assist aid agencies or governments thatfund MFIs (typically at no or low cost) to identify the most efficient in utilizing theirfunding. This is important as some claim that donor dependency breeds inefficiency(Farrington 2000). Secondly, as successful informal MFIs typically grow first intoNGO-MFIs and cooperative MFIs and then later into bank-MFIs and NBFI-MFIs,any differences between the relative efficiency of each MFI type will be identified.To the extent that these outcomes may reflect different regulatory standards, this mayprove of interest to policymakers.

3 Methodology

DEA is a piece-wise linear combination that connects the best practice observationsand forms a convex production possibility set. It was developed by Charnes et al.(1978) and applied to non-profit organizations where the objective of profit maximi-zation and cost minimization may not be considered as the vital factor. DEA also hasthe advantage of working with a small sample size and that does not require priceinformation. As Cooper et al. (2000) suggest, DEA generates cooperation betweenthe policymakers and practitioners in the choice of inputs and outputs and helps detectthe source and amount of inefficiency in each input and output.

3.1 The Model

In this section, we present the two models for measuring the cost efficiency of mi-crofinance institutions. The models differ in their treatment of uncontrollable inputs.Model 1 incorporates two-stage analysis. In the first stage it uses controllable inputsand computes the efficiency scores for the microfinance institutions. In the secondstage of analysis it unravels the effect of uncontrollable inputs from the technical effi-

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ciency. Finally, Model 2 incorporates both uncontrollable and controllable variable asinputs in estimation of DEA efficiency scores.

The data envelopment method follows either a production approach or an interme-diation approach. The DEA method is based on the common assumption of constantreturn to scale6 (CRS) and the alternative assumption of variable return to scale (VRS).CRS provides the same efficiency results in both cases (Avkiran 1999; Coelli 1996).Although VRS model considers inputs increase, it does not expect increase in outputs.We apply both input and output oriented model using the production and intermedi-ation approach. So our analysis is on the basis of constant return to scale measure,Charnes et al. (1978) provided the following formula to measure efficiency.

Max

s∑

r=1

ur yr0

subject to :s∑

r=1

ur yr j −m∑

i=1

vi xi j ≤ 0

m∑

i=1

vi xio = 1; −ur ≤ −ε

−vi ≤ −ε(1)

where, yr j and xi j are positive known outputs and inputs of the jth decision makingunit (DMU) and ur and vi are the variable weights to be determined by solving theabove equation problem.

∑mi=1 vi xio = 1 ensures that it is possible to move from ratio

form to linear programming form and vice versa. ur , vi ≥ ε > 0,∀r, i, are from thenon-Archimedean conditions. This, as given in Charnes and Cooper (1962) insures:

h∗0 =

s∑

r=1

u∗r yr0 (2)

The Eq. (1) interprets as the MFIs objective is to maximize the output subject to unitinput and with the condition that virtual output cannot go beyond virtual input for anyDMU.

On the other hand, the following model provided by Banker et al. (1984) dealt withthe variable return to scale version of DEA.

maxs∑

r=1

ur yro − uo

subjects∑

r=1

ur yr j −m∑

i=1

vi xi j − uo ≤ 0

6 Constant return to scale compares each unit against all other units whereas the variable return to scalecompares each unit against other units of similar size (Avkiran 1999).

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m∑

i=1

vi xio = 1; −ur ≤ −ε

−vi ≤ −ε(3)

u∗ indicates the return to scale possibilities. An u∗ < 0 implies increasing return toscale and u∗ > 0 implies a decreasing return to scale.

Model 1 and Model 2 takes into consideration of the above methodology. However,the second stage analysis in Model 1, we use the DEA measure of efficiency scoresas a dependent variable, and regress on variables which are beyond the control of themicrofinance institutions. Since the efficiency scores are truncated from below at 1 weapply the Tobit model, which produces unbiased and consistent parameter estimationscompared to OLS regression method (McCarty and Yaisawarng 1993 and Dusanskyand Wilson 1995). Our Tobit model is specified as below:

T Ek = rkβ + µk if T E∗k > 1

= 1 if T E∗k ≤ 1 (4)

where, T Ek = technical efficiency of DMU k obtained from the first stage of Model1, T E∗

k = true but unobservable efficiency score for DMU k, rk = [1 zk] is an(1 ∗ (L + 1)) vector of uncontrollable factors plus one, β is an ((L + 1) ∗ 1)) vectorof parameters. µk is a random term, normally distributed. This represents the “puretechnical efficiency” after effects of uncontrollable factors have been unraveled.

Following McCarty and Yaisawarng (1993) we compute the DMU’s pure technical

efficiency∧µ, defined as below:

∧µ = T Ek − rk

∧β (5)

∧µ is not bounded from below at one, it can take a negative, a positive or a zero value. If∧µ is zero then DMU performs equally well compared to the average DMU with sameset of uncontrollable variables.

Our DEA score ranges between 0 and 1. Lower value of efficiency scores presents

poor performance of the DMU k. This implies lower the value of∧µ the better the

performance of DMU k.In Model 2 we ask the research question; given the controllable and uncontrollable

factors which is the most efficient DMU (microfinance institution). Hence Model 2including the uncontrollable factors is specified as follows

maxs∑

r=1

ur yro − uo

subjects∑

r=1

ur yr j −m∑

i=1

vi xi j − uo ≤ 0

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m∑

i=1

vi xio = 1;−ur ≤ −ε

−vi ≤ −ε

−zi ≤ −ε

(6)

3.2 Sample

Our sample is extracted from the MIX Market database. This database providesinformation on 629 MFIs world-wide with 189 from Africa, 141 from South Asia,144 from Latin America and 66 from East Asia and the Pacific. However, our studyconsiders only those MFIs which provide not only credit facilities but also mobilizesavings from members. Second, these MFIs must also provide more than 50% of theirfinancial services (mainly loans and deposit services) to poor households. Consider-ing these factors, we have studies 39 MFIs which fitted with our criteria. This sampleconsists of 39 MFIs including 13 bank MFIs, 8 NBFI-MFIs, 6 cooperatives/creditunions MFIs and 11 NGO-MFIs, and 1 other non classified MFI from Africa, Asiaand Latin America. Finally, we choose 2004 as we find sufficient data for this yearonly to apply the data envelopment analysis. Table 1 contains information about theseMFI

It is evident from Table 1 that bank-MFIs constitutes dominant segment of the totalsample size) followed by unregulated semiformal institutions (NGO-MFIs). Micro-finance comprises from 70 to 100% of their business. Many also mobilize a consid-erable savings from both members and general public,7 for example, Bank RakyatIndonesia in Indonesia, CERUDEB and K-REP in Africa, CMAC in Latin Amer-ica and BRAC and WDFH in South Asia all show savings is their largest fundingsource.

3.3 Inputs and Outputs Specifications

The inputs and outputs variables used in our study and their detailed definitions areprovided in Table 2. Both the production and intermediation approach are used tomeasure the MFI efficiency. The production approach considers the MFI as a factoryproducing services to the poor households. So its outputs8 use the number of accountsrather than any dollar amount.

Several efficiency studies of commercial banks and bank branches, such as Shermanand Gold (1985), Ferrier and Lovell (1990), Berg et al. (1991), have used differentproduction outputs keeping the input (such as labor, fixed assets, capital) remainingthe same. Sherman and Gold (1985), for example, used the number of transactionas the output while Ferrier and Lovell (1990) used the total number of accounts andaccount size as the output.

7 These savings are not considered as a condition to receive current or future loans (MIX market database).8 Studies usually consider the number of transaction or number of branches as output. Our study is based onthe data availability in the Microfinance Information Exchange (MIX) database so we divided the numberof clients into savers per personnel and borrowers per personnel.

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Table 2 Selected inputs and outputs used under production approach and intermediation approach

Approaches Specification Definition

Production approach Inputsa. Labora Number of personnel/staffs which

is defined as the number ofindividuals actively employed bythe MFI. This includes contractemployees or advisors whodedicate the majority of time tothe MFI even if they are not onthe MFI’s roster of employees

b. Cost per borrower Operating expense/average numberof borrowers

c. Cost per saver Operating expense/average numberof savers

Outputs

a. Number of borrowers per staffmember

Total number of activeborrowers/total number ofpersonnel

b. Number of savers per staff member Total number of active savers/totalnumber of personnel

Intermediation approach Inputs

a. Total number of staffs/personnelaNumber of personnel/staffs which

is defined as the number ofindividuals actively employed bythe MFI. This includes contractemployees or advisors whodedicate the majority of time tothe MFI even if they are not onthe MFI’s roster of employees

b. Operating/administrativeexpenses

Administrative expenses excludinginterest expense

Outputs

a. Gross loan portfolio All outstanding principal for alloutstanding client loans includingcurrent, delinquent andrestructure loans but not loansthat have been written off. Itexcludes interest receivable andemployee loans

b. Total savings Total amount of voluntary savings

This table presents the choice of inputs and outputs used to measure the efficiency of different types ofMFIs. The inputs and outputs and their definitions are compiled from the MIXMarket (www.mixmarket.org,(accessed in Jan 2006)) database. The database provides information for individual microfinance institutionsin Africa, Asia and the Latin Americaa We use labor or total number of staffs as an input under both production approach and intermediationapproach. Hence the definition remains the same

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Berg et al. (1991) measured activity in total loan and savings balances along withaverage size and number of accounts. Oral and Yolalan (1990) used five inputs (inphysical term) and four outputs to measure the time expended on various activities.Using a slightly different approach, Laeven (1999) and Pastor et al. (2002) introducedinterest expense and non-interest expense as the inputs in examining efficiency of 10European banks.

Our study uses the production approach with three (3) inputs: labor, cost per bor-rower and cost per saver; the later two inputs are the most important ingredients ofthe operating expense. The two outputs are savers per staff member and borrowers perstaff member. These provide productivity measurement of the MFIs which is indicatedby the number of borrowers per staff and savers per staff.

We consider labor as an input as it reflects time, effort and skills of the workforce. We use cost per borrower and cost per saver as inputs this reflects the oper-ating expenses. Thus, the MFIs would like to reduce cost and protect the borrow-ers-savers and maintain confidence, continue to develop, operate and serve a nichemarket that depends on their services. The overall productivity of the MFI’s staffin terms of managing clients, including borrowers, savers, voluntary savers andother clients. The outputs are the savers per staff member and borrowers per staffmember.

The alternative is the intermediation approach which considers MFIs as financialinstitutions providing financial services to the poor households. It considers the trans-formation of deposits and other loanable funds into credit. Berger and Humphrey(1992) argue that this approach fails to consider the cost of financial transactionsand savings deposits. Elyasiani and Mehdian (1990), Casu and Molynuex (2003) andIsik and Hassan (2003) used deposits as an input which is later on converted intocredit.

Since the MFIs provide voluntary savings facilities to their clients, we consid-ered their deposits as an output. Such consideration is supported by the studies doneby Aly et al. (1990), Berger and Humphrey (1992), Berg et al. (1993), Fried et al.(1993, 1999) and Hassan and Tufte (2001), Guitierrez-Nieto et al. (2006). We con-sidered gross loan portfolio and total savings as two outputs under the intermediationapproach. At the same time, we considered number of personnel and operating ex-penses as important inputs. We consider gross loan portfolio and savings as the outputbecause MFIs will be considered efficient if it has the capability to provide theseservices to it clients.

As our study involves a cross-country analysis, interest expenses and interest incomeare avoided due to the existence of complexities associated with inflation and interestrates in a number of countries.

4 Results

The efficiency of the MFIs is reported on a global scale under both the intermedia-tion and production approaches and the efficiency levels are compared within eachregion.

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4.1 Summary Statistics of Inputs and Outputs

The descriptive statistics for the input and output are provided region-wise in Table 3.We find that average gross loan portfolio and average savings are relatively higherwith the MFIs in the East Asian than their counterparts in the African MFIs.

The East Asian MFIs mobilized savings of some US$ 3.36 billion and operatingexpenses of US$ 0.24 billion. Their relative lowest operating expenses compared toother regions have helped them to rank second for savings mobilization. Further, therewere greater dispersion in total loan portfolio, total savings and operating expenses inEast Asia compared to the other regions.

Table 3 reports the highest average borrowers per staff member and savers per staffmember in Africa against the lowest average borrowers per staff member and sav-ers per staff member in Latin America and East Asia respectively. We find relativelyhigher borrowers per staff member and savers per staff member ratios in Africa andSouth Asia respectively. However, they are relatively lower in South Asia.

The two other variables are the cost per saver and cost per borrower. The maxi-mum cost per borrower and cost per saver are observed in Latin America and EastAsia respectively. For the sake of brevity, we do not report the amount of inputs andoutputs of individual MFIs but we do find Bank Rakyat Indonesia (BRI) mobilizesthe maximum savings and has the largest gross loan portfolio with highest operatingexpense. These demonstrate that East Asia dominates the efficiency frontier.

4.2 Efficiency Estimates of MFIs Using Model 1

The results on the efficiency estimate from running the linear programming model isobtained by considering the intermediation and production approach under the con-stant return to scale (CRS) and variable return to scale (VRS) measure. The CRS isappropriate when an MFI operates at an optimal scale and as such, this measure isnot appropriate to those MFIs which operate at sub-optimal level. The VRS measureis therefore applied to capture sub-optimal level of operations. The results based onestimates of Model 1, under both the intermediation and the production approachesare discussed in Sects. 4.2.1 and 4.2.2.

4.2.1 Intermediation Approach

Efficient MFIs are those which are able to expand their poverty outreach (output ori-ented) and able to achieve operational sustainability (input oriented). Here we discussthe result from the first stage of analysis. Table 4 shows that an African bank-MFI,DECSI, and Latin American, cooperative-MFI, COAC are the most technical efficientMFIs.9

9 Technical efficiency measures the ability of the firm to achieve maximum output for a given set of input.Scale efficiency can be referred to pure technical efficiency. This implies if CRS and VRS are conductedon same data and no difference is found in technical efficiency scores we can define this as pure technicalefficiency (Coelli 1996).

123


Table 3 Descriptive statistics of MFI inputs and outputs by region

Mean Maximum Minimum Standarddeviation

Africa

Intermediation approach (output)

Gross loan portfolio(in 000’ US$) 25362.29 46362.21 385.60 18348.32

Total savings(in 000’ US$) 21426.65 77740.99 31.56 27850.68

Intermediation approach (input)

Total operating expenses(in 000’ US$) 3867.56 15487.76 55.83 4617.44

Number of personnela 438 1670 39 498

Production approach (output)

Borrowers per staff members 198 405 71 112

Savers per staff members 266 779 16 259

Production approach (input)

Cost per borrower(in US$) 99 318 4.5 99

Cost per saver(in US$) 154 1175 7 342

East Asia











Cost per borrower(in US$) 41 106 10.3 35


South Asia











Cost per borrower(in US$) 28 208 6 53


123

78 M. Haq et al.

Table 3 Continued

Mean Maximum Minimum Standarddeviation

Latin America





Total operating expenses(in 000’ US$) 9325.84 28790.85 929.56 9718.67Number of personnela 356 835 70 280





Cost per borrower(in US$) 226 384 60 111


This table presents the descriptive statistics of the inputs and outputs under intermediation approach andproduction approacha We use number of personnel/total number of staffs/labor as an input under both production approach andintermediation approach

However, in stage 1 we also conduct the variable return to scale measure. Our findingshow Bank Solidario, BRI and Grameen Bank are pure technical efficiency, under bothinput and output oriented estimations. The results are reported in the last four columnsof Table 4. Further, in the last four columns we also observe the NBFI-MFIs are lesssuccessful with the exception of CMAC from Latin America showing pure technicalefficiency. However, with the cooperatives/credit union-MFIs, COAC, an unregulatedMFI in Latin America, operates on the efficient frontier across all measures. Amongthe NGO-MFIs, two (2) in South Asia: (ASA in Bangladesh and Wilgamuwa in SriLanka) show pure technical efficiency while the remaining seven (7) NGO-MFIs areinefficient.

Table 4 shows that none of the efficient MFIs has any significant difference ininput-oriented and output-oriented DEA under the VRS measure. However, only theefficiency score of the inefficient MFIs differ between the two methods (Coelli 1996).

Furthermore, Table 5 shows that the overall mean efficiency score is the highest forthe cooperative-MFIs which is followed by bank-MFIs and NGO-MFIs.

This finding is further supported by Fig. 1, where we plot the average efficiencyscores to compare the level of efficiency for different types of MFIs. Figure A showsthe average efficiency scores among the MFIs under the intermediation approach. Thecooperative-MFIs lead in both CRS measure and input-oriented VRS measure andmarginally fall below the banks in output oriented VRS measure. However, the banksshow the overall efficiency score of 1 followed by the cooperatives. The overall leastefficiency score lies with the NGO-MFIs followed by bank-MFIs and cooperative-MFIs. Similarly, the bank-MFIs (input oriented DEA) and the NGO-MFIs (outputoriented DEA) have the maximum dispersion but the cooperative MFIs have the min-imum dispersion in respect of pure technical efficiency scores.

123


Tabl

e4

Effi

cien

cysu

mm

ary

ofin

term

edia

tion

appr

oach

Mod

el1

Mic

rofin

ance

Inst

itutio

ns

Stag

e1

Stag

e2

Stag

e1

Out

puto

rien

ted

VR

Ssc

ale

effic

ienc

yIn

put/o

utpu

tori

ente

dC

RS

tech

nica

lef

ficie

ncy

Ran

kR

esid

ual

∧ µR

ank

Inpu

tori

ente

dV

RS

pure

tech

nica

lef

ficie

ncy

Inpu

tori

ente

dV

RS

scal

eef

ficie

ncy

Out

puto

rien

ted

VR

Spu

rete

chni

cal

effic

ienc

y

Ban

ks-M

FIs

Ruh

unaD

evel

opm

ent

.215

28−0

.095

23.2

17.9

92.2

50.8

60

Nir

dhan

Utth

an.0

4137

−0.2

7535

.055

.753

.047

.873

Firs

tMic

rofin

ance

.177

31−0

.202

32.1

79.9

90.2

05.8

62

K-R

EP

.281

19−0

.050

18.3

24.8

68.5

25.5

36

Ban

kSo

lidar

io.6

604

0.15

56

1.00

0.6

601.

000

.660

CE

RU

DE

B.3

1818

0.01

415

.465

.683

.522

.609

AC

SI.5

518

0.23

64

.552

1.00

0.6

87.8

02

AC

LE

DA

.201

29−0

.130

27.3

54.5

67.4

92.4

08

Ban

coSo

l.5

259

0.02

014

.842

.624

.879

.598

CB

B.2

4525

−0.0

7121

.277

.885

.253

.971

Ban

kR

akya

t.5

696

0.12

67

1.00

0.5

691.

000

.569

Gra

mee

nB

ank

.473

100.

125

81.

000

.473

1.00

0.4

73

DE

CSI

1.00

0n.

a.n.

a.n.

a.1.

000

1.00

01.

000

1.00

0

NB

FI-

MF

Is

EB

S.3

3317

0.00

216

.533

.625

.594

.560

AM

K.0

8835

−0.2

4334

.098

.903

.089

.995

CM

AC

.855

10.

326

31.

000

.855

1 .00

0.8

55

CM

F.1

0934

−0.1

9531

.138

.787

.136

.799

123

80 M. Haq et al.

Tabl

e4

cont

inue

d

Mic

rofin

ance

Inst

itutio

ns

Stag

e1

Stag

e2

Stag

e1

Out

puto

rien

ted

VR

Ssc

ale

effic

ienc

yIn

put/o

utpu

tori

ente

dC

RS

tech

nica

lef

ficie

ncy

Ran

kR

esid

ual

∧ µR

ank

Inpu

tori

ente

dV

RS

pure

tech

nica

lef

ficie

ncy

Inpu

tori

ente

dV

RS

scal

eef

ficie

ncy

Out

puto

rien

ted

VR

Spu

rete

chni

cal

effic

ienc

y

Caj

aN

or.5

617

0.03

210

.561

1.00

0.6

2.9

06

SEE

DS

.39

120.

080

9.3

91.9

97.4

43.8

80

FIN

CO

MU

N.1

2833

−0.4

2637

.132

.966

.196

.651

AV

FS.1

9130

−0.1

2524

.254

.751

.202

.946

Coo

pera

tive

-MF

Is

VY

CC

U.7

942

0.47

91

.943

.842

.933

.851

CO

AC

1.00

0n.

a.n.

a.n.

a.1.

000

1.00

01.

000

1.00

0

CM

S.5

965

0.19

45

.76

.784

.805

.740

AC

CO

VI

.430

11−0

.047

17.4

35.9

90.5

97.7

21

AC

EP

.750

30.

348

2.7

81.9

61.8

06.9

31

KC

.372

15−0

.128

26.4

00.9

30.3

82.9

74

NG

O-M

FIs

NSS

C.2

4922

−0.0

6719

.299

.834

.262

.952

CE

P.2

7920

−0.0

7222

.283

.985

.279

.999

TY

MFU

ND

.376

130.

025

12.3

90.9

64.3

81.9

87

BR

AC

.223

27−0

.125

25.6

07.3

68.6

71.3

33

ASA

.376

130.

028

111.

000

.376

1.00

0.3

76

CM

ED

FI.0

7836

−0.4

2236

.109

.709

.080

.972

AC

CD

C.3

3416

0.02

413

.426

.783

.367

.909

WD

FH.2

426

−0.0

7020

.26

.922

.244

.983

123


Tabl

e4

cont

inue

d

Mic

rofin

ance

Inst

itutio

ns

Stag

e1

Stag

e2

Stag

e1

Out

puto

rien

ted

VR

Ssc

ale

effic

ienc

yIn

put/o

utpu

tori

ente

dC

RS

tech

nica

lef

ficie

ncy

Ran

kR

esid

ual

∧ µR

ank

Inpu

tori

ente

dV

RS

pure

tech

nica

lef

ficie

ncy

Inpu

tori

ente

dV

RS

scal

eef

ficie

ncy

Out

puto

rien

ted

VR

Spu

rete

chni

cal

effic

ienc

y

Kas

hf.2

4922

−0.1

3027

.253

.988

.254

.982

WA

GE

S.2

5821

−0.1

4429

.266

.971

.259

.996

Wilg

amuw

a.1

6332

−0.1

4730

1.00

0.1

631.

000

.163

CD

S.2

4922

−0.2

1533

.287

.866

.259

.959

Ove

rall

aver

age

0.38

3−0

.031

.510

.805

.531

.786

Min

imum

0.04

1−.

426

0.05

50.

163

0.04

70.

163

Max

imum

1.4

791

11

1

Thi

sta

ble

repo

rts

the

resu

ltsof

the

DE

Aef

ficie

ncy

scor

esun

der

inte

rmed

iatio

nap

proa

ch.

We

draw

aco

mpa

riso

nof

the

effic

ienc

ysc

ores

amon

gsa

mpl

eba

nk-M

FIs,

NB

FI-M

FIs,

Coo

pera

tive-

MFI

san

dN

GO

-MFI

sac

ross

Afr

ica,

Asi

aan

dL

atin

Am

eric

a

123

82 M. Haq et al.

Table 5 Descriptive statistics of efficiency scores (intermediation approach)

Banks-MFIs NBFI-MFIs Cooperative-MFIs NGO-MFIs

Mean

Input/output oriented (TE) .4043 .332 .538 .240

Input oriented (PTE) .559 .388 .587 .490

Output oriented (PTE) .605 .41 .605 .484

Overall mean score .523 .377 .577 .405

Minimum




Overall minimum score .041 .088 .249 .019

Maximum

Input/output oriented (TE) 1 .855 1 .376

Input oriented (PTE) 1 1 1 1

Output oriented (PTE) 1 1 1 1

Overall maximum score 1 1 1 1

Standard deviation




This table presents the descriptive statistics of the efficiency scores of different types of MFIs across thesample regions. The mean, minimum, maximum and standard deviation are reported for input and outputoriented technical efficiency at constant return to scale (CRS) and for input and output oriented pure technicalefficiency under variable return to scale (VRS)PTE Pure technical efficiency, TE Technical efficiency

We now turn our discussion to the second stage where we incorporate a socialeconomic factor: percentage of rural population. Hence, MFI identified as inefficientin the above discussion may not infact reflect that they are unable to reach the poorhousehold and achieve sustainability. Rather they may face a difficult challenge whichmay be beyond the MFIs control. For example, it may be difficult for the MFIs to reachall the rural population simply because there may be a group of household which mayreflect moral hazard problem. Moral hazard problem/adverse selection arises as theMFIs are unable to identify the group of borrowers’ actual intention of repaying theloan. This uncontrollable factor can lead to waste and mismanagement of resourcesfor a MFI.

Table 4 draws the difference in the rank orderings of the efficiency scores betweenfirst stage and second stage. We find that CMAC drops to third ranking from first in thesecond stage of analysis and VYCCU improves from a second place to the first place.Hence, we may have considered CMAC to be on the best practice frontier but as soonas we control for the rural population percentage, we observe CMAC’s achievementlevels are not sufficiently higher than expected given its inputs and uncontrollablefactor. K-REP, provides an example of better MFI despite being an inefficient MFI,the drop in inefficiency is relatively lower compared to the other inefficient MFIs.

123


Comparison of Efficiency

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

NGOsCooperativesNBFIsBanks

microfinance institutions

aver

age

effi

cien

cy s

core

TEInput oriented PTEOutput oriented PTE

Comparison of efficiency scores

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

aver

age

effi

cien

cy s

core

s TEPTE(input)PTE(output)

NGOsCooperativesNBFIsBanksmicrofinance institutions

A

B

Fig. 1 a Comparison of efficiency scores (intermediation approach). b Comparison of efficiency scores(production approach)

4.2.2 Production Approach

The efficiency summary of production approach in Table 6 is quite different from theintermediation approach. We first discuss stage 1 (column 1) and then discuss the puretechnical efficiency scores that we estimate under stage 1 (last four columns). Whileboth technical (column 1) and pure technical efficiencies (last four columns) are foundwith the three (3) MFIs (Ruhuna, DECSI and Wilgamuwa), only pure technical effi-ciency is found with the two (2) MFIs (ASA and COAC). A number of MFIs appearsto be efficient under production approach and these are; for instance, CEP in Vietnam,ACCDC and WDFH in Sri Lanka, CMF and WAGES in Africa.

The pure technical efficient frontier is dominated by South Asian NGO-MFIs. Largebank-MFIs like BRI, Banco Solidario and Grameen Bank, which were efficient underintermediation approach, are now inefficient under production approach. Yet, bank-MFIs such as Ruhuna, DECSI, and NGO-MFIs such as CEP and Wilgamuwa are allefficient under production approach. Under the input oriented VRS measure, FINCO-

123

84 M. Haq et al.

Tabl

e6

Effi

cien

cysu

mm

ary

ofpr

oduc

tion

appr

oach

usin

gM

odel

1

MFI

sSt

age

1St

age

2St

age

1O

utpu

tori

ente

dV

RS

scal

eef

ficie

ncy

Inpu

t/out

put

orie

nted

CR

Ste

chni

cale

ffici

ency

Ran

kR

esid

ual

∧ µR

ank

Inpu

tori

ente

dV

RS

pure

tech

nica

lef

ficie

ncy

Inpu

tori

ente

dV

RS

scal

eef

ficie

ncy

Out

put

orie

nted

VR

Spu

rete

chni

cal

effic

ienc

y

Ban

k-M

FIs

Ruh

una

Dev

elop

.1.

000

n.a.

n.a.

n.a.

1.00

01.

000

1.00

01.

000

Nir

dhan

Utth

an.

.338

14−0

.115

23.4

81.7

03.4

83.7

Firs

tMic

rofin

ance

.020

34−0

.252

31.0

41.4

72.1

90.1

03

K-R

EP

.102

26−0

.291

33.1

07.9

54.4

30.2

38

Ban

kSo

lidar

io.0

3331

−0.1

1421

.033

.993

.402

.082

CE

RU

DE

B.1

1325

−0.3

9536

.126

.901

.411

.276

AC

SI.4

516

−0.0

0210

.833

3.5

41.5

41.8

34

AC

LE

DA

.019

35−0

.374

35.0

63.3

05.1

43.1

35

Ban

coSo

l.0

2532

−0.1

2225

.032

.761

.299

.082

CB

B.4

1510

−0.0

3814

.478

.867

.76

.546

Ban

kR

akya

t.1

5223

−0.0

3815

.167

.913

.645

.236

Gra

mee

nB

ank

.438

80.

095

8.5

56.7

89.7

86.5

58

DE

CSI

1.00

0n.

a.n.

a.n.

a.1.

000

1.00

01.

000

1.00

0

NB

FI-

MF

Is

EB

S.2

4419

−0.1

4926

.314

.777

.591

.413

AM

K.3

8912

−0.0

0412

.533

.730

.733

.531

CM

AC

.066

27−0

.069

18.1

14.5

82.5

51.1

21

CM

F.3

3415

−0.1

7428

1.00

0.3

341.

000

.334

Caj

aN

or.0

2433

−0.1

1119

.032

.748

.309

.078

123


Tabl

e6

cont

inue

d

MFI

sSt

age

1St

age

2St

age

1O

utpu

tori

ente

dV

RS

scal

eef

ficie

ncy

Inpu

t/out

put

orie

nted

CR

Ste

chni

cale

ffici

ency

Ran

kR

esid

ual

∧ µR

ank

Inpu

tori

ente

dV

RS

pure

tech

nica

lef

ficie

ncy

Inpu

tori

ente

dV

RS

scal

eef

ficie

ncy

Out

put

orie

nted

VR

Spu

rete

chni

cal

effic

ienc

y

SEE

DS

.120

24−0

.358

34.3

13.3

82.2

31.5

18

FIN

CO

MU

N.0

1036

−0.1

1422

.020

.510

.200

.052

AV

FS.3

9111

−0.0

6217

.497

.785

.588

.665

Coo

pera

tive

MF

Is

VY

CC

U.2

6717

−0.1

8629

.412

.650

.567

.471

CO

AC

.256

180.

109

71.

000

.256

1.00

0.2

56

CM

S.1

8522

−0.0

5116

.356

.520

.505

.366

AC

CO

VI

.042

30−0

.122

24.0

56.7

54.2

74.1

53

AC

EP

.046

29−0

.190

30.0

46.9

85.3

78.1

21

KC

.474

50.

325

4.8

04.5

89.9

3.5

1

NG

O-M

FIs

NSS

C.4

487

−0.0

0513

.535

.837

.675

.663

CE

P.7

452

0.41

12

1.00

0.7

451.

000

.745

TY

MFU

ND

.578

40.

244

5.6

80.8

49.7

12.8

12

BR

AC

.340

13−0

.003

11.6

25.5

44.5

42.6

27

ASA

.787

10.

444

11.

000

.787

1.00

0.7

87

CM

ED

FI.1

9721

0.04

89

.241

.816

.504

.39

AC

CD

C.3

1116

−0.1

6727

1.00

0.3

111.

000

.311

WD

FH.2

0420

−0.2

7432

1.00

0.2

041.

000

.204

123

86 M. Haq et al.

Tabl

e6

cont

inue

d

MFI

sSt

age

1St

age

2St

age

1O

utpu

tori

ente

dV

RS

scal

eef

ficie

ncy

Inpu

t/out

put

orie

nted

CR

Ste

chni

cale

ffici

ency

Ran

kR

esid

ual

∧ µR

ank

Inpu

tori

ente

dV

RS

pure

tech

nica

lef

ficie

ncy

Inpu

tori

ente

dV

RS

scal

eef

ficie

ncy

Out

put

orie

nted

VR

Spu

rete

chni

cal

effic

ienc

y

Kas

hf.4

339

0.16

16

.474

.914

.664

.652

WA

GE

S.6

443

0.40

83

1.00

0.6

441.

000

.644

Wilg

amuw

a1.

000

n.a.

n.a.

n.a.

1.00

01.

000

1.00

01.

000

CD

S.0

6128

−0.1

1120

.065

.940

.438

.140

Ove

rall

aver

age

0.33

−0.0

550.

490.

700.

630.

45

Min

imum

0.01

−0.3

950.

020.

200.

140.

05

Max

imum

10.

444

11

11

Thi

sta

ble

repo

rts

the

resu

ltsof

the

DE

Aef

ficie

ncy

scor

esun

derp

rodu

ctio

nap

proa

ch.W

edr

awa

com

pari

son

ofth

eef

ficie

ncy

scor

esam

ong

sam

ple

bank

-MFI

s,N

BFI

-MFI

s,C

oope

rativ

e-M

FIs

and

NG

O-M

FIs

acro

ssA

fric

a,A

sia

and

Lat

inA

mer

ica

123


Table 7 Descriptive statistics of efficiency scores (production approach)

Bank-MFIs NBFI-MFIs Cooperative-MFIs NGO-MFIs

Mean




Overall mean efficiency .413 .358 .517 .707

Minimum




Overall minimum score .019 .01 .042 .197

Maximum

Input/output oriented (TE) 1 .391 .745 1

Input oriented (PTE) 1 1 1 1

Output oriented (PTE) 1 1 1 1

Overall maximum score 1 1 1 1

Standard deviation




This table presents the descriptive statistics of the efficiency scores of different types of MFIs across thesample regions under production approach. The mean, minimum, maximum and standard deviation arereported for both input and output oriented technical efficiency at constant return to scale (CRS) and forinput and output oriented pure technical efficiency under variable return to scale (VRS)PTE Pure technical efficiency, TE Technical efficiency.

MUN is the least efficient. In order to be efficient, these MFIs should reduce theirinputs by 98% as done by DECSI and Wilgamuwa.

Similarly, the output-oriented measure shows that ACLEDA in Cambodia is theleast efficient MFI. As shown on Table 7, the NGO-MFIs have the highest overallmean efficiency score followed by the cooperative-MFIs. The bank-MFIs, however,are better than the NBFI-MFIs.

Among the bank-MFIs and NGO-MFIs, there is at least one efficient MFI underboth CRS and VRS measures. There is highest dispersion in the bank-MFIs’ efficiencyscore. Figure B shows that NGO-MFIs are the most productive under all measuresfollowed by the cooperative-MFIs. The least efficient are the non-bank MFIs.

From Table 6 we can see that there are not many differences in ranking amongthe MFIs between first stage and second stage analysis. However, we find the lowestdrop among the inefficient MFIs is for ACSI, however this leads to a drop of rankingfrom 6th to 10th position. We also find Grameen bank, ACLEDA, AMK, CEP, ASAand Wages are non-movers and hence remain at the same position for both technicalefficiency and pure technical efficiency or “residual”. We can also observe that SEEDS

123

88 M. Haq et al.

show a 41% drop in their position while FINCOMUN has climbed to position 22 frombottom 36.

4.3 Efficiency Estimates of MFIs Using Model 2

Tables 8 and 9 present efficiency scores and their rank ordering from Model 2, in whichboth controllable and uncontrollable inputs are incorporated. Our findings show thatthe magnitudes of the efficiency scores are higher in Model 2 compared to Model 1.10

Our uncontrollable variable is the percentage of rural population to total population.This may also represent the urbanization rate for each region.

Table 8 presents the result of the efficiency scores under intermediation approach.The mean score of technical efficiency under constant return to scale is approximately50%, however since we consider that MFIs do not operate in optimal level so we alsoreport the variable return to scale results for the pure technical efficiency and scaleefficiency under both output and input oriented and the mean score ranges between0.65 and 0.86.

The rank orderings are quite similar to those based on residual values∧µ in Model 1.

The Pearson correlation coefficient is 84%, indicates that the two rank ordering arepositively correlated at 1% significance level. Comparing individual rankings betweenmodel 1 and model 2 we find remarkable difference which is the change in rankingfor ASA, BRI, Grameen Bank, and CMAC. These are now ranked as 1 or the mostefficient. However, the peer summary reflects that these DMUs are efficient by defaultwhich means that each DMU uses a unique combination of inputs and outputs suchthat it is compared only to itself when the efficiency score is calculated. Thus wecannot consider them to be the role model for the inefficient DMUs.

Table 9 presents the result of the efficiency scores under production approach. Themean score of technical efficiency under constant return to scale is approximately 64%,however the mean efficiency score for pure technical efficiency and scale efficiencyunder variable return to scale, both output and input oriented; ranges between 0.73and 0.88.

The rank orderings are quite similar to those based on residual values∧µ in Model 1.

The Pearson correlation coefficient is 76%, indicates that the two rank ordering arepositively correlated at 1% significance level. Comparing individual rankings betweenModel 1 and Model 2, there appears to be some differences. Bank Rakyat Indonesia,CMAC, CMF and COAC have improved in their efficiency rankings. They are now themost efficient with a score of 1. However, based on the peer analysis we find that theseMFIs cannot be identified as the role model for the inefficient MFIs as they utilize aunique combination of input and output such that it is only compared to itself.

10 Model 1 where we do not incorporate the uncontrollable variable as an input.

123


Tabl

e8

Effi

cien

cysu

mm

ary

ofin

term

edia

tion

appr

oach

Mod

el2

MFI

sIn

put/o

utpu

tor

ient

edC

RS

tech

nica

leffi

cien

cy

Ran

kIn

puto

rien

ted

VR

Spu

rete

chni

cal

effic

ienc

y

Inpu

tori

ente

dV

RS

scal

eef

ficie

ncy

Out

puto

rien

ted

VR

Spu

rete

chni

cal

effic

ienc

y

Out

put

orie

nted

VR

Ssc

ale

effic

ienc

y

Ban

ks-M

FIs

Ruh

unaD

evel

opm

ent

0.23

529

0.35

30.

664

0.25

0.93

7

Nir

dhan

Utth

an0.

043

370.

374

0.11

60.

047

0.92

3

Firs

tMic

rofin

ance

0.17

732

0.52

50.

337

0.20

50.

862

K-R

EP

0.38

918

0.42

40.

916

0.52

50.

741

Ban

kSo

lidar

io1

11

11

1

CE

RU

DE

B0.

446

150.

465

0.95

90.

522

0.85

5

AC

SI0.

658

110.

659

0.99

90.

687

0.95

8

AC

LE

DA

0.44

316

0.44

70.

991

0.49

20.

9

Ban

coSo

l0.

862

60.

868

0.99

40.

879

0.98

1

CB

B0.

245

270.

616

0.39

80.

253

0.97

1

Ban

kR

akya

t1

11

11

1

Gra

mee

nB

ank

11

11

11

DE

CSI

1n.

a.1

11

1

NB

FI-

MF

Is

EB

S0.

505

140.

533

0.94

70.

594

0.85

AM

K0.

088

350.

446

0.19

80.

089

0.99

5

CM

AC

11

11

11

CM

F0.

109

340.

397

0.27

40.

136

0.79

9

Caj

aN

or0.

645

121

0.64

51

0.64

5

SEE

DS

0.39

170.

460.

849

0.44

30.

88

123

90 M. Haq et al.

Tabl

e8

cont

inue

d

MFI

sIn

put/o

utpu

tor

ient

edC

RS

tech

nica

leffi

cien

cy

Ran

kIn

puto

rien

ted

VR

Spu

rete

chni

cal

effic

ienc

y

Inpu

tori

ente

dV

RS

scal

eef

ficie

ncy

Out

puto

rien

ted

VR

Spu

rete

chni

cal

effic

ienc

y

Out

put

orie

nted

VR

Ssc

ale

effic

ienc

y

FIN

CO

MU

N0.

2130

10.

211

0.21

AV

FS0.

191

310.

781

0.24

40.

210.

907

Coo

pera

tive

-MF

Is

VY

CC

U0.

794

71

0.79

40.

998

0.79

5

CO

AC

1n.

a.1

11

1

CM

S0.

703

90.

760.

926

0.80

50.

873

AC

CO

VI

0.55

713

0.79

50.

70.

597

0.93

3

AC

EP

0.75

80.

781

0.96

10.

806

0.93

1

KC

0.37

220

10.

372

10.

372

NG

O-M

FIs

NSS

C0.

249

240.

776

0.32

10.

270.

925

CE

P0.

279

220.

479

0.58

30.

279

0.99

9

TY

MFU

ND

0.37

619

0.63

70.

590.

405

0.92

8

BR

AC

0.67

100.

70.

958

0.67

10.

999

ASA

11

11

11

CM

ED

FI0.

078

360.

998

0.07

80.

160.

485

AC

CD

C0.

334

210.

880.

379

0.36

70.

909

WD

FH0.

2428

0.55

40.

432

0.24

40.

983

Kas

hf0.

249

240.

533

0.46

80.

254

0.98

2

WA

GE

S0.

258

230.

596

0.43

30.

259

0.99

5

Wilg

amuw

a0.

163

331

0.16

31

0.16

3

123


Tabl

e8

cont

inue

d

MFI

sIn

put/o

utpu

tor

ient

edC

RS

tech

nica

leffi

cien

cy

Ran

kIn

puto

rien

ted

VR

Spu

rete

chni

cal

effic

ienc

y

Inpu

tori

ente

dV

RS

scal

eef

ficie

ncy

Out

puto

rien

ted

VR

Spu

rete

chni

cal

effic

ienc

y

Out

put

orie

nted

VR

Ssc

ale

effic

ienc

y

CD

S0.

249

240.

778

0.32

0.25

90.

959

Ove

rall

aver

age

.50

.58

.86

.73

.65

Min

imum

.043

.353

.078

.047

.163

Max

imum

11

11

1

Thi

sta

ble

repo

rts

the

resu

ltsof

the

DE

Aef

ficie

ncy

scor

esun

der

inte

rmed

iatio

nap

proa

ch.

We

draw

aco

mpa

riso

nof

the

effic

ienc

ysc

ores

amon

gsa

mpl

eba

nk-M

FIs,

NB

FI-M

FIs,

Coo

pera

tive-

MFI

san

dN

GO

-MFI

sac

ross

Afr

ica,

Asi

aan

dL

atin

Am

eric

a

123

92 M. Haq et al.

Tabl

e9

Effi

cien

cysu

mm

ary

ofpr

oduc

tion

appr

oach

usin

gM

odel

2

MFI

sIn

put/o

utpu

tori

ente

dC

RS

tech

nica

lef

ficie

ncy

Ran

kIn

puto

rien

ted

VR

Spu

rete

chni

cal

effic

ienc

y

Inpu

tori

ente

dV

RS

scal

eef

ficie

ncy

Out

puto

rien

ted

VR

Spu

rete

chni

cal

effic

ienc

y

Out

puto

rien

ted

VR

Ssc

ale

effic

ienc

y

Ban

ks-M

FIs

Ruh

una

Dev

elop

.1

n.a.

1a1

1a1

Nir

dhan

Utth

an.

0.47

426

0.82

40.

575

0.48

30.

981

Firs

tMic

rofin

ance

0.13

435

0.51

70.

259

0.19

90.

673

K-R

EP

0.36

529

0.47

10.

775

0.45

60.

801

Ban

kSo

lidar

io0.

641

170.

828

0.77

40.

642

0.99

8

CE

RU

DE

B0.

428

0.46

70.

858

0.41

10.

975

AC

SI0.

507

240.

925

0.54

90.

541

0.93

8

AC

LE

DA

0.10

836

0.43

60.

248

0.14

90.

724

Ban

coSo

l0.

476

250.

826

0.57

60.

476

0.99

8

CB

B0.

7610

0.81

40.

934

0.76

1

Ban

kR

akya

t1

11a

11a

1

Gra

mee

nB

ank

0.82

88

0.96

20.

861

0.84

10.

985

DE

CSI

1n.

a.1a

11a

1

NB

FI-

MF

Is

EB

S0.

643

160.

677

0.95

0.64

40.

999

AM

K0.

656

150.

732

0.89

70.

733

0.89

6

CM

AC

11

1a1

1a1

CM

F1

11a

11a

1

Caj

aN

or0.

535

221a

0.53

51a

0.53

5

SEE

DS

0.21

533

0.68

10.

316

0.23

10.

932

123


Tabl

e9

cont

inue

d

MFI

sIn

put/o

utpu

tori

ente

dC

RS

tech

nica

lef

ficie

ncy

Ran

kIn

puto

rien

ted

VR

Spu

rete

chni

cal

effic

ienc

y

Inpu

tori

ente

dV

RS

scal

eef

ficie

ncy

Out

puto

rien

ted

VR

Spu

rete

chni

cal

effic

ienc

y

Out

puto

rien

ted

VR

Ssc

ale

effic

ienc

y

FIN

CO

MU

N0.

423

271a

0.42

31a

0.42

3

AV

FS0.

556

210.

864

0.64

30.

588

0.94

5

Coo

pera

tive

-MF

Is

VY

CC

U0.

562

200.

861

0.65

20.

570.

985

CO

AC

11

1a1

1a1

CM

S0.

639

180.

675

0.94

70.

642

0.99

5

AC

CO

VI

0.31

931

0.81

20.

393

0.32

10.

993

AC

EP

0.36

330

0.57

20.

635

0.47

40.

767

KC

11

1a1

1a1

NG

O-M

FIs

NSS

C0.

675

140.

880.

767

0.67

50.

999

CE

P1

11a

11a

1

TY

MFU

ND

0.75

111

1a0.

751

1a0.

751

BR

AC

0.57

319

0.96

0.59

70.

586

0.97

8

ASA

0.78

79

1a0.

787

1a0.

787

CM

ED

FI0.

731

120.

999

0.73

20.

921

0.79

4

AC

CD

C0.

311

321a

0.31

11a

0.31

1

WD

FH0.

204

341a

0.20

41a

0.20

4

Kas

hf0.

7213

0.92

30.

780.

742

0.97

WA

GE

S1

11a

11a

1

Wilg

amuw

a1

n.a.

1a1

1a1

123

94 M. Haq et al.

Tabl

e9

cont

inue

d

MFI

sIn

put/o

utpu

tori

ente

dC

RS

tech

nica

lef

ficie

ncy

Ran

kIn

puto

rien

ted

VR

Spu

rete

chni

cal

effic

ienc

y

Inpu

tori

ente

dV

RS

scal

eef

ficie

ncy

Out

puto

rien

ted

VR

Spu

rete

chni

cal

effic

ienc

y

Out

puto

rien

ted

VR

Ssc

ale

effic

ienc

y

CD

S0.

5123

0.77

90.

655

0.52

20.

978

Ove

rall

aver

age

.638

.859

.728

.734

.880

Min

imum

.108

0.43

60.

204

.149

.204

Max

imum

11

11

11

Thi

sta

ble

repo

rts

the

resu

ltsof

the

DE

Aef

ficie

ncy

scor

esun

derp

rodu

ctio

nap

proa

ch.W

edr

awa

com

pari

son

ofth

eef

ficie

ncy

scor

esam

ong

sam

ple

bank

-MFI

s,N

BFI

-MFI

s,C

oope

rativ

e-M

FIs

and

NG

O-M

FIs

acro

ssA

fric

a,A

sia

and

Lat

inA

mer

ica

aIn

dica

tes

the

MFI

isef

ficie

ntby

defa

ultt

hati

sit

has

aun

ique

com

bina

tion

ofin

puta

ndou

tput

sre

lativ

eto

othe

rsin

the

sam

ple,

thus

isco

mpa

red

only

toits

elf

inm

easu

ring

the

degr

eeof

prod

uctiv

eef

ficie

ncy

123


5 Conclusion

This study investigated the cost efficiency of MFIs (bank-MFIs, NBFI-MFIs, coop-erative-MFIs and NGO-MFIs) in Africa, Asia, and the Latin America using the dataenvelopment analysis (DEA). The MFIs were compared using the intermediation andproduction approaches to identify which MFI type is the most efficient in minimizingcosts and providing financial services to poor households.

Our findings show that under the intermediation approach four (4) out of thirteen(13) bank-MFIs are both input and output oriented, pure technical efficient and scaleefficient. Under production approach, six (6) out of twelve (12) NGO- MFIs are foundto be the most efficient. We find more MFIs show VRS pure technical efficiencythan either CRS technical efficiency or VRS pure technical efficiency under boththe intermediation and production approach. Five (5) out of twelve (12) NGO-MFIsare pure technical efficient under production approach while three (3) out of thirteen(13) bank-MFIs are pure technical efficient under intermediation approach. Only one(1) bank-MFI and one (1) cooperative-MFI under intermediation approach and two(2) bank-MFIs and one (1) NGO-MFI under production approach are CRS technicalefficient and VRS pure technical efficient.

The results discussed above may suggest that high level of cost efficiency may havedecreased due to the amount of non-performing loans specifically for bank-MFIs underthe intermediation approach. In other words, cost efficient managers are better manag-ing their loan customers and properly monitoring MFIs’ operating costs. Furthermore,the levels of efficiency have much more to do with efficient utilization of resourcesrather than scale of production.

In conclusion, we can suggest NGO-MFIs may be promoted in developing regionsas these MFIs are found to be the most efficient under production approach. This resultis not surprising given the NGO-MFIs’ dual objectives of alleviating poverty throughincreased out reach and simultaneously achieving financial sustainability. Over theyears NGO-MFIs have learnt to develop staff productivity, to increase branching anddistribution system, to build outstanding portfolio quality and to extend relationshipbanking culture to the poor. As these institutions are mostly either unregulated or lessregulated than other MFIs, policymakers should approach further NGO-MFI regula-tion with care so that this efficiency is not hampered. Nevertheless, some bank-MFIsare quite efficient in providing microfinance particularly DECSI in Africa. As morebank-MFIs are established they may have the competitive advantage as financial inter-mediaries in areas like access to local capital as well as the global financial markets.So while bank-MFIs are the most efficient type of MFI at present under intermedi-ation approach, NGO-MFIs may eventually perform better as an intermediary in thelong–run if proper regulation and supervision are in place.

Acknowledgment We thank an anonymous referee for helpful and substantial comments. We also thankJiro Akahori (Editor). The usual caveats apply.

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