does capital and financing structure have any relevance to the performance of microfinance...

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Does capital and financing structurehave any relevance to the performanceof microfinance institutions?Ashim Kumar Kar aa Department of Economics , Hanken School of Economics ,Helsinki , FinlandPublished online: 09 Aug 2011.

To cite this article: Ashim Kumar Kar (2012) Does capital and financing structure have anyrelevance to the performance of microfinance institutions?, International Review of AppliedEconomics, 26:3, 329-348, DOI: 10.1080/02692171.2011.580267

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Does capital and financing structure have any relevance to theperformance of microfinance institutions?

Ashim Kumar Kar*

Department of Economics, Hanken School of Economics, Helsinki, Finland

(Received 30 November 2009; final version received 9 March 2011)

This paper aims to explore the impact of capital and financing structure on theperformance of microfinance institutions (MFIs) from an agency theoretic stand-point. GMM and IV estimations with instruments have been performed using apanel dataset of 782 MFIs in 92 countries for the period 2000–2007. Resultsconfirm the agency theoretic claim that an increase in leverage raises profit-effi-ciency in MFIs. The study also finds that cost efficiency deteriorates withdecreasing leverage. Likewise, the negative significant impact of leverage ondepth of outreach can also be explained. However, the study finds that capitalstructure does not have any noticeable impact on breadth of outreach and nei-ther is it significantly related with women’s participation as loan clients.

Keywords: microfinance institutions; outreach; financial performance; panel dataestimation; GMM estimation; cross-country study

JEL Classifications: G21; G32

1. Introduction

What should be the capital structure of a firm? Has the proportion of debt usage anyrelevance to the individual firm’s value? Concerns relating to these capital- andfinancing-structure issues are of enormous significance to any organization. Firms’capital structure primarily indicates the mix of long-term debt and equity used intheir operation. Financing structure, in contrast, relates to how firms’ assets – suchas short-term borrowings, long-term debt and owner’s equity – are financed. How-ever, these issues in terms of specialized lending organizations like microfinanceinstitutions (MFIs) have started attracting scholarly attention only in the recent past.1

MFIs have extended the frontiers of institutional finance to the entrepreneurialpoor, mostly women, providing easy access to credit and financial services (Armen-daritz de Aghion and Morduch 2005). Now this growing industry is going to reachwell over 100 million clients globally (Cull, Demirguc-Kunt, and Morduch 2009)and is achieving impressive repayment rates on loans (Cull, Demirguc-Kunt, andMorduch 2007). Despite the success of many MFIs and amid increasing commer-cialization of this sector, however, a large fraction of poor people in many develop-ing countries still remains unreached (Christen, Rosenberg, and Jayadeva 2004).

*Email: [email protected]

International Review of Applied EconomicsVol. 26, No. 3, May 2012, 329–348

ISSN 0269-2171 print/ISSN 1465-3486 online� 2012 Taylor & Francishttp://dx.doi.org/10.1080/02692171.2011.580267http://www.tandfonline.com

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Primarily due to high operating costs and capital constraints within the MFIs, thereis indeed a challenge to meet from the supply-side (Helms 2006).

MFIs’ operational objectives differ and, as a result, two conflicting paradigmshave emerged: ‘welfarist’ and ‘institutionist’.2 The institutionist proponents ofmicrofinance promote competition and financial sustainability to maximize the cov-erage or breadth of outreach of MFIs (Woller, Dunford, and Woodworth 1999; Mor-duch 2000). Accordingly, and considering the huge potential of microfinance aswell, numerous traditional NGO-MFIs have transformed themselves into formalizedbanks and non-bank financial intermediaries with diverse and innovative combina-tions of products. This has resulted in MFIs’ extended links with capital marketsand, consequently, mutual funds as part of the shareholder structure have been setup. Since the presence of debt exerts pressure on management to ensure efficiencyand profitability, these developments have several implications for the capital struc-ture, operations and performance of MFIs (Armendaritz de Aghion and Morduch2005).

In a standard principal–agent set-up, agency costs mainly arise due to diver-gence and separation of ownership and control and differences in managers’ objec-tives. Corporate governance theory predicts that leverage affects agency costs andconsequently influences firm-performance. Under the agency costs hypothesis, alow equity-asset ratio or high leverage reduces agency costs of outside equity andthus increases firm value by restraining, encouraging or compelling managers to actmore for upholding shareholders’ interest (Berger and Bonaccorsi di Patti 2006).Hence, theoretically, capital structure has an impact on a firm’s performance againstthe position held by Modigliani and Miller (1958).3

Thus, agency costs and the structure of capital flows to firms raise particularlyimportant research questions. However, empirical evidence on this issue is mixed inthe existing literature, where only banks, SMEs or large and listed corporate firmsin the developed economies are sampled.4 In microfinance literature this type ofresearch is particularly scanty indeed. To the best of our information, no study sofar has addressed this issue, rigorously employing data on a comprehensive scale.Therefore, it would be highly significant and motivating to examine how the capitaland financing structure of MFIs influence their performance and this paper aims atassessing such impacts, if any, using a large cross-country cross-MFI panel datasetfocusing mainly on agency theoretic views.

The paper proceeds as follows. Section 2 describes the data and justifies selec-tion of explanatory and outcome variables, the empirical set-up and the regressionapproach. Then the estimated results are discussed in Section 3. Finally, Section 4offers some concluding remarks.

2. Data and empirical approach

2.1. Data

The study utilizes a comprehensive panel dataset on 782 MFIs in 92 countries cov-ering a period of eight years – 2000 to 2007. Each MFI has data for a minimum of4 years to a maximum of 8 years with most of them containing 8 years’ data. Thedata are collected from individual MFI profiles as reported to the MicrofinanceInformation Exchange (MIX), a not-for-profit private organization that aims to pro-mote exchange of information in the microfinance industry.5 So far, this is the most

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detailed publicly available data on financial, portfolio and outreach performance ofMFIs on a global scale. During the time of data collection in July 2009, it hadlisted the profiles of more than 1300 MFIs from over 112 countries. However, notall of them could meet the requirements of this study. The selection criteria requiredall MFIs to have at least a level-3 diamond disclosure rating on the MIX Market.6

Additionally, following Cassar and Holmes (2003) MFIs with over 100% leveragewere eliminated so as to avoid problems associated with negative values and nega-tive-equity firms, which may appear as a consequence of using book values ratherthan market values. Again, MFIs without data on all required aspects wereexcluded.7 Finally, a sample of 782 MFIs in 92 countries was available for analysis.MFIs from all six developing regions8 and of all types – non-profit NGOs, non-bank financial institutions, banks, co-operatives/credit unions and others – at variousstages of their life cycle with diversified loan methodologies are included in thedatabase.

2.2. Measures of MFI performance: The outcome variables

MFIs are specialized financial institutions with some peculiarities and unlike con-ventional financial institutions (CFIs) they are constrained by double bottom lines:meeting social obligations (the first bottom-line) and obtaining financial self-reliance(the second bottom-line) (Hartarska 2005). For CFIs, diversified measures of firmperformance including financial ratios, stock market return and their volatility andTobin’s-q have been used to test the predictions of different agency cost hypotheses(Berger and Bonaccorsi di Patti 2006). However, MFIs differ from CFIs in manyother ways. Located primarily in poverty-ridden rural areas, MFIs pave the way thatbanks should go to the poor not the other way round. Unlike development banks inprevious times, MFIs take a market-based approach to provide small-sized, mostlycollateral-free and women-focused lending for serving the poor on a sustainablebasis. MFIs’ innovative loan products and methodologies also attempt to win overthe typical credit market problems of asymmetric information and moral hazard.These help meeting their social and financial obligations (Armendaritz de Aghionand Morduch 2005).

Consequently, unlike definitions given in corporate finance, performance ofMFIs bears a slightly different connotation and frequently encompasses two broadaspects – self-sustainability and outreach to the poor – both of which have addi-tional dimensions. MFIs’ self-sustainability is evident, among others, in their profit-efficiency and cost-efficiency. Profitability or sustainability of an MFI is measuredby financial self-sufficiency (FSS), operational self-sufficiency (OSS), return onassets (ROA) and return on equity (ROE). Both FSS and OSS basically measurehow well the MFI can cover its costs through financial and operating revenues.ROA and ROE measure how well the MFI uses its total assets and equity capitalrespectively to generate returns (Hartarska 2005). So, justifiably this study employsROE as a proxy for overall-financial-sustainability or the profit-performance ofMFIs. Again, operating expenses per dollar lent (OELP) are defined as the ratio ofoperating cost over gross loan portfolio and we used this variable as a proxy formeasuring cost-efficiency. Since loans are small and there may be other fixed costsper borrower, it is important to differentiate this cost from operating cost per bor-rower (CPB) as described in Table 1.

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MFIs’ outreach to the poor, in turn, is measured in two further extended dimen-sions – breadth and depth (Navajas et al. 2000; Schreiner 2002; Hartarska 2005).Breadth in effect means the number of clients to whom microfinance services areprovided, and is normally expressed in terms of (natural) logarithm of activeborrowers. MFIs’ breadth of outreach is clearly different from a market penetrationtype of measurement that is used in CFIs because market penetration is just thenumber of customers as a percentage of the total. Since MFIs are constrained bydouble bottom lines and, generally, attempts to meet the supply-side challenge,increasing coverage or the size of poor-clientele base is very important. Hence, thenumber of active borrowers as the proxy for MFIs’ breadth of outreach is justifiablyemployed in this paper. Depth of outreach means the quality of outreach to the poorand is generally measured by three variables – average loan amount, average loanamount adjusted by GNI (or GDP) per capita and percent of female loan clients.All of them have been used in this exercise.

2.3. The explanatory variables

2.3.1. Leverage

Leverage indicates the proportion of a firm’s total capital contributed by trade credi-tors and lenders and demonstrates the firm’s capacity for debt repayment. The capi-tal-assets-ratio (CAR), i.e. the ratio of equity capital to gross total assets, is used asa standard inverse measure of leverage, especially in banking research because reg-ulatory attentions are paid to capital-assets ratios (Berger and Bonaccorsi di Patti,2006). Regarding financial performance, the agency costs hypothesis states thatincreasing leverage or decreasing capital-assets ratio is associated with a reductionin the agency costs of outside equity and an improvement in firm performance.Concerning outreach, leverage is hypothesized to be positively related with breadthof outreach and negatively with depth of outreach. When depth of outreach is mea-sured by the share of female clients, however, the hypothesis remains uncertain – itcan be both positive and negative.

Debt-equity-ratio (DER), a standard measure for the long-term health of anorganization, also measures leverage that indicates the extent to which the busi-ness relies on debt financing. A high financial leverage or DER indicates a possi-ble difficulty in paying interest and principal while obtaining more funding. Anunlevered firm can be seen as an all-equity firm, whereas a levered firm is madeup of ownership equity and debt. Employed in this study, the hypothesized rela-tionship of CAR with MFIs’ profit-efficiency and outreach as stated above.

2.3.2. Financing structure

Debt, the financing structure variable, has been defined as the ratio of all borrow-ings to total assets. The hypothesized relationship between profit-performance anddebt is similar to that of profit-performance and CAR.

2.3.3. Focus on lending

For MFIs, the ratio of gross loan portfolio to total assets (Loans) is an indication oftheir focus on lending as these funds could have been utilized otherwise. So, this

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ratio is more than just a measure for risk exposure. Although the hypothesized rela-tionship between outreach and loans is uncertain, to control for MFIs’ focus onsocial mission this variable is utilized.

2.3.4. Risk

It has been hypothesized that MFIs that are profit-inefficient and poor at operationsmight also be poor at risk management. This yields a negative relationship betweenprofit-efficiency and risk. In this study we have defined risk mainly in three ways.First, risk is defined as the deviation from mean profitability, where mean profitabil-ity is the mean of ROA. Second, we used the Z-risk measure (Hannan and Han-weck 1988) given by (ROAi + Ei /Ai)/rROAi, where subscript i denotes MFI number,rROAi is the standard deviation of #ROA, E is equity and A is the MFI’s totalassets. The lower is Z the higher is its probability of default. Third, we also utilizedthe standard portfolio-at-risk past 30 days (PAR30) as another measure for risk ofan MFI. PAR30 captures the accounting convention that loans exceeding 30 daysoverdue pose an unacceptably high risk of non-repayment. Z-risk and PAR30 arehypothesized to be inversely associated with profit-efficiency or firm performancein general. However, in terms of outreach measures the risk hypothesis is generallyuncertain.

2.3.5. Size

To control for the effects of differences in technology, investment opportunities anddiversification and differences associated with MFIs’ varying scales of operation weinclude size – defined as the natural logarithm of total assets – as one of the mainexplanatory variables in all regressions. Size hypothesis is uncertain too – it can bepositively as well as negatively related with MFI-performance as measured by out-reach and sustainability.

2.3.6. Age

MFI-Age variable enters the regressions on sustainability and outreach assumingthat older micro-banks may benefit from learning-curve effects. Productivity andefficiency improvement can be considered as the whole organization’s learning pro-cess. Generally, the pattern involves first speeding up and then slowing down, asthe practically feasible level of methodology development is reached. Therefore,age is hypothesised to affect MFIs’ financial performance initially positively andthen negatively. However, based on existing literature, it is hypothesised to be nega-tively associated with the depth of outreach measures, but positively with thebreadth dimension.

The included Gross National Income per capita (GNI-pc) variable is an impor-tant exogenous factor that captures the impact of the country size and macroeco-nomic environment and hence can influence MFIs’ breadth of outreach. Wehypothesize that GNI-pc adversely affects the breadth dimension, as increased percapita GNI is meant for better economic conditions of the clients and therefore theirparticipation in MFIs’ credit programmes may decline. MFIs’ efficiency indicatorcost-per-borrower (CPB) and expenditure performance indicator operating-expense-ratio (OER) have also been controlled in different model specifications. In addition,


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the included dummy variable ‘regulated’ basically captures the differences in theregulatory environment. As the regulatory regime may not change overnight it isassumed to be constant over the entire period (2000–2007), resulting in a time-invariant dummy variable. Regulatory regime is also hypothesized to be positivelyrelated with outreach performance and both positively and negatively associatedwith financial performance variables. Five regional dummy variables have beenincluded – EAP, EECA, MENA, SA and SSA – to control for regional differencesin MFI-performance and operation, if any. LAC region is used as the control cate-gory. Explanatory variables along with their hypothesized signs are described inTable 1.

2.4. Reverse causality: Firm performance to capital structure

As described before, agency costs hypothesis states that capital structure affectsfirm performance. Conversely, the literature also suggests that firm performanceand capital structure can be jointly determined by some unobserved variable, sowe need to take care of the possible reverse causation from MFI-performance tocapital structure. There are several reasons why firm performance may affect capi-tal structure, especially when some of the Modigliani-Miller market perfectionassumptions are violated. ‘Efficiency-risk’ and ‘franchise-value’ hypotheses explainwhy differences in firm performance, particularly in specialized banking institutionsincluding MFIs, may move the equity-capital-ratio marginally up or down. Bergerand Bonaccorsi di Patti (2006) provide a detailed agency theoretic analysis of thisreverse causation.

2.5. The empirical model

The regression model in implicit form is:

PERFit ¼ f ðLEVit;ZitÞ þ uit; i ¼ 1; 2; � � � ;N ; t ¼ 1; 2; � � � ;T ð1Þ

Where subscript i stands for individual MFIs and t denotes time, respectivelythe cross-section and time-series dimensions of a variable. PERF is a vector of per-formance indicators for MFI i in time t and LEV is a vector of measurements forleverage ratios. The use of LEV in this form, as an inverse measure of leverage, isstandard in banking research in part because of the regulatory attention paid to capi-tal ratios. Since MFIs are considered quasi-banks we used this specific relationship.The vector Zit (=Z1it + Z2i) contains other characteristics that are likely to influencethe outcome variables. Z1it include measures of time-varying characteristics, forinstance, risk, MFI size, MFI age etc; whereas, Z2i include the time-invariant vari-ables such as the regulatory environment and regional variations. Finally, uit is themean-zero idiosyncratic disturbance term that varies over both time and individual.In addition, the estimation model includes an individual or cross-section level (fixedeffects) error term independent of time. This time-invariant, error term containsunobserved effects, such as the MFIs’ distinctive way of performing actions. Inother words, this means the unobserved heterogeneity of an individual MFI.

The agency costs hypothesis predicts that dPERF/dLEV < 0 as higher capital-assets ratios or lower leverage reduce pressure on managers to maximize value,aggravating agency problems between these parties and owners and reducing profit-

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Table1.

Definitio

nsof

independentvariablesandtheirhypothesised

signs(P

=Positive;N

=Negative).

Variable

Explanatio

nHypothesis

Financial

performance

Outreach

Capital-Assets-Ratio

Adj.totalequity/Adj.totalassets

P/N

P/N

Debt-Equity

-Ratio

Adj.totalliabilities/Adj.totalequity

P/N

P/N

Loans

Gross

loan

portfolio

/Adj.totalassets

P/N

P/N

Debt

Borrowings/Adjustedtotalasset

P/N

P/N

Risk

Deviatio

nsfrom

meanprofi

tability(ROA)

NP/N

Z-Risk

(ROAi+Ei/A

i)/r R

OAi

NP/N

PAR30

Portfoliosoverduepast30

days

+Re-negotiatedportfolio

/Adjusted

grossloan

portfolio

NP/N

Costperborrow

erAdj.op.expense/Adj.av.no.of

borrow

ers

NP

Op.

expenseratio

Adj.op.expense/Adj.averagetotalassets

P/N

P/N

Size

Natural

logof

totalassets

P/N

P/N

MFI-age

Years

ofexperience

asan

MFI

P/N

P/N

GNI-pc

Gross

NationalIncomepercapita

–N

Regulated

Adummy=1iftheMFIisregulated

N/P

PEAP

Adummy=1iftheMFIisin

EAPregion

EECA


EECA

region

MENA


MENA

region

LAC


LAC

region

SA


SA

region

SSA


SSA

region


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efficiency. However, when leverage becomes relatively high, or the capital-assetsratio becomes sufficiently low, the sign of the relationship may switch, as theagency costs of outside debt overwhelm the reduction in agency costs of outsideequity, so further increases in leverage result in higher total agency costs (Bergerand Bonaccorsi di Patti 2006).

2.6. Tests and methods

The model empirically tested for each of the dependent variables yit is as follows:

Yit ¼ X 0itbþ ðaþ u0iÞ þ eit ð2Þ

Here, a is the mean of unobserved heterogeneity, ui is heterogeneity specific toMFI i, eit is the remaining firm-year heterogeneity, yit is the outcome variable, andXit and b are the matrix of explanatory variables and the vector of coefficientsrespectively.

To check the poolability, a joint F-test (Baltagi 2008) reveals that both individ-ual and time effects are statistically significant at 1% level in our panel data, thusrejecting the homogeneity assumption across MFIs and time. The endogeneity biasof profit-efficiency and financial leverage ratio is overcome finding a set of relevantinstruments independent of the error term. In order to identify the coefficients bj,we needed at least as many instruments (L) as regressors (K). As suggested by Dea-ton (1995), instruments were constructed among the lagged explanatory variablessince the independent variables are all simultaneous and, therefore, the lagged vari-ables were not related to dependent variables. Since L > K, we have a set of over-identifying restrictions. The instruments’ independence of the error term is thentested with Hansen’s (1982) J-test which is distributed as w2 with (L – K) degreesof freedom. A high value of w2 (and very low p-value) indicates that some of theinstruments are still correlated with the error term, and therefore, the endogeneityproblem persists.

As suggested by Baltagi (2008) and Hahn, Hausman, and Kuersteiner (2004), toovercome the potential problems associated with outliers with bad leverage andweak instruments in unbalanced panel data, justifiably we used the k-class (forinstance, LIML and IV) estimators in our case. Besides, since one- and two-stepGMM estimators are usually robust to violations of homoscedasticity and normality,we perform the two-step GMM estimations with heteroscedasticity- and autocorrela-tion-consistent (HAC) standard errors. Since we have large N and small T panels,the GMM estimator allows for arbitrary heteroscedasticity and serial dependenceusing the optimal weighting matrix (Wooldridge 2002).

Then fixed-effects 2SLS (FE2SLS), error components 2SLS (EC2SLS) (Baltagi1981) and LIML methods have been used to estimate model (2). Initially, applyingHausman tests as the generally accepted way of choosing between fixed-effects andrandom-effects give significant values. Therefore, use of the FE estimates has beenemphasized. However, the FE estimator is inconsistent when the explanatory vari-ables encounter the problem of possible endogeneity. To deal with this problem, werun the EC2SLS estimator, which is a matrix weighted average of between-effects2SLS and FE2SLS. Following Baltagi (2006), Hausman tests based on the differ-ence between FE2SLS and EC2SLS were applied and significant w2 values wereobtained. This rejects the null hypothesis that EC2SLS yields a consistent estimatorand, consequently, we choose to stick on and report the FE estimates only. The FE

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estimates are presented basically in three flavours – FE2SLS, GMM fixed-effectsand LIML fixed-effects. As significant and similarly-signed regression coefficientsenhance the credibility of the results, we reported all of them.

3. Econometric evidence

3.1. Descriptive facts

Table 2 presents the descriptive statistics regarding both outcome variables and theexplanatory variables. Mean values of many of the variables can be interpreted asthe percentage of firms in the category. Most of the MFIs around the globe aremoderately leveraged as shown by the mean of 0.38 of capital-assets-ratio (CAR).The standard deviations alongside the minimum and maximum values of the majorexplanatory variables other than the regulated dummy and the regional dummiesindicate that the microfinance industry is indeed highly disproportionately distrib-uted. Again, the size variable shows that nearly 15% of all MFI assets are fixed innature. Therefore, the majority of the MFIs own sizable amounts of current as wellas intangible assets of other types.

The mean values of only 0.023 and 0.075 for return on assets and return onequity respectively point to the inadequate financial performance of the sampledMFIs globally. Again, the standard deviation score of 1.34 and the spread of mini-mum and maximum values of ROE spanning from –44.11 to 34.23 suggest thatonly a very few MFIs, and not the majority, are performing well. Therefore, argu-ably MFI-performance is rather widely dispersed and the overall mean performancehas been driven only by a handful of well-performing MFIs. Similar explanationsalso apply for ROA. Relevant authorities regulate 59% of microfinance institutions.With clearly dissimilar sizes, most of the sampled MFIs are matured since theiraverage age of operation is about 11.5 years.

Other performance variables such as outreach (both breadth and depth), risk anddefault rates are relatively encouraging, suggesting that the institutions under surveyare evenly matched. In particular, the average default rate in terms of portfolios-at-risk past 30 days (PAR30) is about 6%, which is quite similar to those found byMersland and Strom (2009). In an anecdotic sense, the measure of Z-risk is also onthe higher side (nearly 17%), indicating a lower default likelihood as a lower Z-riskimplies higher probability of default. With regards to the regional distribution ofMFI samples, the highest – about 27% of MFIs – are sampled from the LACregion, while the MENA region represents 5.7%, the lowest. Similarly, about 12%of sampled MFIs are from EAP, 20% from EECA, 15.1% from SA and 20.3% fromSSA regions. These samples sufficiently represent the respective number of MFIs inoperation in these developing regions.

3.2. Discussion of regression results

3.2.1. Performance

Overall financial performance (ROE). Table 3 offers the main results for equation(2) that tests the predictions of the agency costs hypothesis for the effects of CARon profit-efficiency or overall financial performance. In order to explore possiblenon-linear association between profit-efficiency and leverage, estimates both withand without the CAR2 variable are presented. Although not many coefficients are


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Table2.

Descriptiv

estatistics.

Variable

NMean

Std.Dev.

Min

Max

Dependent

variables

Outreachindicators

No.

ofactiv

eborrow

ers

3375

60484.18

361622.2

166707000

Average

loan

(GNI-adj.)

3374

0.917

2.977

0.01

94.71

Fem

aleborrow

ers

3032

0.652

0.270

01

Perform

ance

indicators

Returnon

assets

3375

0.023

0.084

�0.5

0.44

Returnon

equity

3375

0.075

1.335

�44.11

34.23

Op.

exp.

per$lent

3375

0.262

0.212

02.33

Explanatory

variables

Capital-assets-ratio

3375

0.375

0.274

01

Debt-equity-ratio

3373

6.836

27.434

0647.29

Loans-assets-ratio

(Loans)

3375

0.745

0.168

0.029

1.280

Debt

2000

0.357

0.269

01.00

Risk

3375

�0.012

0.084

�0.429

0.511

Z-risk

3297

16.764

18.820

�3.123

198.431

PAR30

3116

0.058

0.087

01.05

Costperborrow

er3335

169.729

373.578

09084

Op.

expenseratio

3375

0.180

0.126

01.04

Totalassets

3375

2.74e+07

9.63e+07

21831

2.25e+09

Size

3375

15.410

1.801

9.991

21.535

MFIage

3297

11.527

8.404

057

GNIpercapita

3374

1614.829

1610.123

89.796

11674.72

Regulated

3324

0.586

0.493

01

EAP

3375

0.118

0.323

01

EECA

3375

0.203

0.402

01

LAC

3375

0.267

0.442

01

MENA

3375

0.057

0.232

01

SA

3375

0.151

0.358

01

SSA

3375

0.203

0.402

01

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Table3.

Profit-efficiency

regressions(dependent

variable:logof

return

onequity).

GMM-FE

FE2S

LS

(1)

(2)

(3)

(4)

(5)

(6)

CAR

�28.848⁄

�33.708⁄

⁄⁄�8

.313

�34.254⁄

⁄⁄�3

3.616⁄

⁄⁄�1

4.068⁄

⁄(12.285)

(9.130)

(6.505)

(9.229)

(6.718)

(4.502)

CAR2

19.319

⁄26.044

⁄⁄⁄

21.052

⁄⁄26.125

⁄⁄⁄

(9.050)

(7.219)

(7.716)

(5.727)

Debt

�0.103

�4.350

�1.834

�7.450

⁄⁄(5.807)

(3.545)

(4.297)

(2.319)

Debt2

�5.044

�5.288

(4.801)

(4.044)

Z-risk

�0.030

�0.002

�0.080

0.013

0.001

�0.027

(0.071)

(0.048)

(0.064)

(0.065)

(0.055)

(0.062)

Log

PAR30

�16.285

�12.412

�8.610

�17.217

�11.498

�11.271

(19.063)

(16.901)

(18.752)

(14.388)

(10.771)

(13.839)

Log

age

�0.637

0.498

�0.051

�2.095

0.488

�2.341

(3.087)

(2.033)

(3.089)

(2.362)

(1.753)

(2.313)

Log

age2

�0.242

�0.138

�0.538

0.070

�0.158

0.059

(0.863)

(0.609)

(0.815)

(0.642)

(0.487)

(0.625)

Size

�0.327

�0.695

�0.137

�0.268

�0.623

⁄�0

.239

(0.452)

(0.366)

(0.401)

(0.283)

(0.250)

(0.274)

Constant

54.185

39.112

39.064

(34.435)

(25.378)

(32.888)

Hansen’sJ

0.247

0.623

0.253

Hausm

anw2

24.6

20.38

25.50

P-value

0.003

0.005

0.000

N1051

1314

1051

1207

1521

1207

Notes:Coefficients

forthe‘regulated’andregion

aldu

mmiesareno

tpresented.

HAC

standard

errors

arein

theparentheses.

⁄Statistically

sign

ificant

atthe

⁄ 10%

,⁄⁄5%

and

⁄⁄⁄ 1%

levels.Hausm

an’stestsfor(FE2S

LS–ES2S

LS)aregivenin

w2values.ROEisfirstsquaredandthen

transformed

innaturallogs.Suchtransformationgives

betterdistribu

tionandincreasednu

mberof

observations.


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significant, interesting results appear in both significant and non-significant findings.The coefficients for the leverage indicator CAR and its quadratic term are alwayssignificant except in model 3. The negative significant coefficients for CAR alongwith positive significant coefficients for CAR2 suggest an increasing and thendecreasing effect of CAR on profit-performance and hence, the relationship isclearly non-linear. The equal (negative) signs of the CAR and ‘Debt’ coefficients inall models confirm the credibility of the results, while their significant coefficientsvalidate the predictions of the agency cost hypothesis. In other words, for this effi-ciency measure, the data are consistent with the predictions of the agency costshypothesis that an increase in leverage – i.e. a reduction in the capital-assets-ratio –may raise profit-efficiency. Although managements of MFIs are different from othercorporate practices, this result is generally in line with Berger and Bonaccorsi diPatti (2006).

The ‘Debt’ leverage and the quadratic term ‘Debt2’ have generally an insignifi-cant, but negative and offsetting, impact on profit-performance. In order to differen-tiate which effect is larger, we disentangled the effects of CAR on profitability fromthe effects of ‘Debt’ and Debt2 on the same. As expected, CAR and ‘Debt’ havealways similar negative signs across the models but the coefficient for ‘Debt’ is sig-nificant only in model 6, suggesting an increase in the debt or liability position isassociated with a decrease in profitability. This is explained by the fact that long-term debts are relatively more expensive, and therefore employing high proportionsof them could lead to low profitability. Thus, it appears that borrowings or debtgenerally cannot effectively exert pressure on MFI-management on the whole tolead to more returns on equity capital, all else being equal. These results generallysupport earlier findings of Fama and French (2002).

The coefficients for both of the risk assessment variables – PAR30 and Z-risk –generally have the expected negative signs, but they are insignificant. The variablesize has negative and rarely significant coefficients, which suggest that larger andolder MFIs may not always perform very well in terms of profitability. However,this phenomenon should be interpreted with caution, since the effects of size andage are conditional on CAR and other explanatory variables. Large-scale and oldermicro-banks may be more efficient than they appear because they tend to havelower values of CAR, which improves their efficiency.

Cost-efficiency: Operating-cost-per-dollar-lent (OELP) is a vital instrument toassess the financial health of an MFI in general and efficiency and productivity ofthe MFI in particular. Table 4 presents the main results for equation (2) that teststhe predictions of the agency costs hypothesis for the effects of leverage ratios oncost-efficiency as measured by OELP. We see that the inverse leverage variableCAR is positively signed throughout, but is never significant at any conventionallevel. Indeed, this can again be explained by the agency cost hypothesis that ahigh capital-assets ratio that ensures low leverage is associated with a rise inoperating expenses, resulting in a deterioration of cost performance of an MFI.Debt-equity-ratio, another measure for leverage, confirms this phenomenon byasserting a negatively signed coefficient; this is, however, insignificant. Addingthe quadratic terms CAR2 and DER2 yields negative insignificant coefficients andalter the respective signs of CAR and DER coefficients. The loan default variable,PAR30, is negatively signed throughout and observed significant in models 5 and6. These imply that the impacts of loan defaults on cost performance are negative,which is sensible as, by definition, PAR30 involves some renegotiating of loan

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Table4.

Cost-efficiency


variable:operatingexpenses

perdollar).

GMM-FE

LIM

L-FE

FE2S

LS

(1)

(2)

(3)

(4)

(5)

(6)

CAR

0.113

0.485

0.110

0.393

0.110

0.363

(0.087)

(0.566)

(0.089)

(0.639)

(0.061)

(0.425)

CAR2

�0.211

�0.142

�0.121

(0.425)

(0.476)

(0.312)

DER

�0.032

1.170

�0.034

0.935

�0.033

0.856

(0.184)

(1.148)

(0.188)

(1.329)

(0.108)

(0.862)

DER2

�1.565

�1.213

�1.100

(1.336)

(1.607)

(1.019)

Log

PAR30

-8.648

�9.216

�8.704

�9.161

�8.653

⁄⁄�8

.058

⁄(10.034)

(9.296)

(10.478)

(12.167)

(3.346)

(3.275)

Size

�0.031

�0.024

�0.032

�0.024

�0.032

⁄⁄�0

.023

(0.026)

(0.025)

(0.026)

(0.029)

(0.011)

(0.012)

Log

age

�0.012

�0.014

�0.012

�0.014

�0.012

�0.014

(0.068)

(0.069)

(0.068)

(0.070)

(0.037)

(0.039)

Log

age2

�0.002

�0.002

�0.001

�0.002

�0.002

�0.004

(0.027)

(0.026)

(0.027)

(0.028)

(0.013)

(0.013)

Constant

20.937

⁄⁄9.319⁄

(7.814)

(7.602)

Hansen’sJ-test

0.970

0.819

0.971

Hausm

anw2

14.34

17.04

P-value

0.0261

0.0296

N1811

1811

1811

1811

1941

1941

Notes:Coefficients


aldu

mmiesareno

tpresented.

Hausm

an’s

testsfor(FE2S

LS–ES2S

LS)aregivenin

w2values.HAC

standard

errors

arein

theparentheses.

⁄ Statistically

sign

ificant

atthe

⁄ 10%

,⁄⁄5%

and

⁄⁄⁄ 1%

levels.


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portfolios and thus PAR30 should directly affect CPB, but not OELP in a similarfashion.

Size, age and age2 variables are equally negatively signed in all model specifi-cations. The negative significant size variable in models 5 implies that while MFIsrely on donor funds they still operate efficiently and are currently realizing efficien-cies in terms of reducing operating costs because of economies of scale. A negativecoefficient for the age variable was expected. This could mean that as an MFImatures, possibly it learns how to run operations incurring fewer and fewer costs.However, along with the quadratic term the age variable is never significant.

3.2.2. Outreach

Breadth of outreach. Breadth of outreach regression estimates, where the number ofactive borrowers is the dependent outreach variable, are presented in Table 5. As isevident in our results, inverse leverage is negatively related with MFIs’ breadth ofoutreach, but they are insignificant. Another measure of leverage – debt-equity-ratio– impacts positively and insignificantly on breadth of outreach as expected. The‘loans’ variable has positive and highly significant coefficients across models, sug-gesting that in the global microfinance industry the higher the focus on lending, the

Table 5. Breadth of outreach regressions (dependent variable: log of number of activeborrowers).

FE2SLS GMM-FE LIML-FE

CAR �0.110 �0.088 �0.116(0.187) (0.225) (0.232)

DER 0.235 0.172 0.237(0.392) (0.516) (0.532)

Log CPB �0.618⁄⁄⁄ �0.632⁄⁄⁄ �0.617⁄⁄⁄(0.044) (0.065) (0.068)

Size 1.060⁄⁄⁄ 1.063⁄⁄⁄ 1.061⁄⁄⁄(0.028) (0.035) (0.036)

Loans 0.675⁄⁄⁄ 0.650⁄⁄⁄ 0.676⁄⁄⁄(0.065) (0.106) (0.110)

Log Z-risk 0.051 0.044 0.052(0.045) (0.054) (0.057)

Log OER 0.764⁄⁄⁄ 0.769⁄⁄⁄ 0.763⁄⁄⁄(0.044) (0.062) (0.064)

Log age 0.101 0.106 0.101(0.057) (0.079) (0.079)

Log GNI-pc 0.324⁄⁄⁄ �0.316⁄⁄⁄ �0.326⁄⁄⁄(0.061) (0.079) (0.080)

Constant �1.670⁄⁄⁄(0.433)

Hansen’s J-test 0.617 0.619Hausman w2 142.06P-values 0.000Observations 1727 1513 1513

Notes: Coefficients for the ‘regulated’ and regional dummies are not presented. Time-effects wereincluded. Hausman’s tests for (FE2SLS – ES2SLS) are given in w2 values. HAC Standard errors are inthe parentheses. ⁄Statistically significant at the ⁄10%, ⁄⁄5% and ⁄⁄⁄1% levels.

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better the outreach level and this increased focus helps in extracting premiums fromthe loans previously disbursed. This premium then contributes to the firm’s incomeflow and profitability that could later be used for debt-servicing. Again, higher levelof outreach enables MFIs to enjoy economies of scale essentially as a result ofreduction in average cost of operation. Furthermore, an increase in outreach couldalso lead to product diversification for diverse clientele groups and this enables anMFI to cushion itself against risk (Kyereboah-Coleman 2007).

Table 5 also shows that the clientele base significantly decreases with anincreased level of average cost, which is justified because increasing the clientelebase with unfavourable costs cannot be advantageous. Thus, inefficient MFIs thatfight for reducing per borrower costs should try to avoid improving the breadthdimension of outreach. However, this needs utmost care so that possible concernsfor mission drift are not compromised.

The positive, but insignificant, coefficients for the MFI age variable in Table 5indicate that when other variables are taken into consideration, the individual MFItends to increase the clientele base over time, which is reasonable. The positive andhighly significant coefficients for the size variable confirm reliability of the resultsand support the idea that reliance on donor funds motivates the MFIs effectively towiden their clientele base. Highly significant positive coefficients for the OER vari-able reliably confirm that increased outreach level goes at par with high operatingexpenses as both old and numerous new clients are engaged in the loan pro-grammes. As expected, GNI-pc coefficients are negative and highly significant, sug-gesting that improved economic status adversely affects MFIs’ breadth dimensionof outreach.

Depth of outreach (adjusted average loan). Table 6 presents the depth of out-reach regression results where depth of outreach is measured by average loan sizeadjusted by GNI per capita. We observe that the slope coefficients for capital-asset-ratio, debt-equity-ratio, loans-to-assets-ratio, risk, size and MFI age variables are allmostly highly significant and equally signed in all model specifications. Thus, dif-ferent panel data estimations are largely consistent and trustworthy. Hansen’s J-sta-tistic of over-identifying restrictions shows that instruments are not correlated withthe error term, confirming their relevance.

The coefficients for the inverse measure of leverage, i.e. the capital-assets ratio,are negative and significant, suggesting that a reduction in the capital-assets-ratio oran increase in leverage increases the depth of outreach. However, firm-value or per-formance of any corporate entity and that of an MFI can never be similar. So, thispoint should be explained with utmost care, especially when one relates the resultswith the predictions of the agency costs hypothesis. As mentioned earlier, unlikecorporate entities, an MFI’s success is assessed mainly through its social and finan-cial performance. So, the applicability of the agency costs hypothesis in a crudesense may not have great significance here. However, as far as MFIs’ success isalso measured through the poverty level of the poor clients, this result may help inexplaining the point that an increased level of leverage leads to better social perfor-mance.

However, there is a counterbalancing negative significant impact of the debt-equity-ratio variable. Although the sign is as expected, it is important to measurethe overall impact of both of these measures of leverage on depth of outreach. Inmodels 2, 3, 5 and 6, CAR and DER are controlled separately. However, when theyare separated, unlike CAR, DER loses its significance, although they always have


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Table6.

Depth

ofoutreach


variable:logof

averageloan

adjusted

percapita

GNI).

GMM-FE

FE2S

LS

(1)

(2)

(3)

(4)

(5)

(6)

CAR

�0.249

⁄�0

.153

�0.265

⁄⁄⁄

�0.162

⁄⁄(0.109)

(0.090)

(0.069)

(0.051)

DER

�0.493

⁄�0

.190

�0.500

⁄⁄�0

.168

(0.226)

(0.251)

(0.190)

(0.174)

Loans

0.634⁄

⁄⁄0.582⁄

⁄⁄0.628⁄

⁄⁄0.634⁄

⁄⁄0.582⁄

⁄⁄0.626⁄

⁄⁄(0.131)

(0.127)

(0.129)

(0.078)

(0.076)

(0.078)

Risk

�0.418

⁄�0

.321

�0.398

⁄�0

.411

⁄⁄⁄

�0.317

⁄⁄⁄

�0.386

⁄⁄⁄

(0.172)

(0.175)

(0.187)

(0.099)

(0.090)

(0.099)

PAR30

0.108

0.063

0.100

0.088

0.047

0.071

(0.176)

(0.164)

(0.177)

(0.106)

(0.101)

(0.106)

Size

0.147⁄

⁄0.138⁄

⁄0.165⁄

⁄0.143⁄

⁄⁄0.136⁄

⁄⁄0.161⁄

⁄⁄(0.052)

(0.049)

(0.051)

(0.030)

(0.029)

(0.029)

Log

age

�0.228

⁄�0

.226

⁄�0

.216

⁄�0

.225

⁄⁄⁄

�0.223

⁄⁄⁄

�0.209

⁄⁄⁄

(0.107)

(0.106)

(0.106)

(0.062)

(0.061)

(0.062)

Constant

�3.058

⁄⁄⁄

�2.986

⁄⁄⁄

�3.480

⁄⁄⁄

(0.482)

(0.447)

(0.444)

Hansen’sJ

0.35

0.46

0.22

Hausm

anw2

125.53

234.24

72.61

P-values

0.000

0.000

0.000

N2217

2261

2217

2295

2332

2295

Notes:Coefficients


aldu

mmiesareno

tpresented.

Tim

e-effectswereinclud

ed.Hausm

an’s

testsfor(FE2S

LS–ES2S

LS)aregivenin

w2

values.HAC

standard

errors

arein

theparentheses.

⁄ Statistically

sign

ificant

atthe

⁄ 10%

,⁄⁄5%

and

⁄⁄⁄ 1%

levels.

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negative signs. This means that a higher debt scenario improves the social perfor-mance of an MFI since a decrease in adjusted loan balance is considered to be animprovement in depth of outreach. The loans-to-assets variable has highly signifi-cant and positive coefficients all through, which indicates that an increased focuson lending is harmful for depth of outreach. This confirms that reaching the verypoor, or alternatively maintaining better-quality outreach, cannot always be ensuredthrough such increased emphasis on lending. This is again a case for possible con-cerns for mission drift and this phenomenon fails to support the conjecture that reli-ance on donor and other competitive sources of funding motivates MFIs to operateefficiently so that the poor can always be reached.

Table 6 shows that the coefficients for the PAR30 variable are always insignifi-cant. This reaffirms that loan default is irrelevant as long as depth of outreach isconcerned. The positive significant size coefficients again raises, like the loans-to-assets variable, the already-familiar concerns for mission drift that larger MFIs tendto provide bigger adjusted average loans. This may so happen as increasing sizeand increased focus on lending finally may have similar consequences. Again, aswe expected and as supported in relevant literature, the MFI age variable has nega-tive and significant coefficients all through. These in effect mean that as MFIsmature they tend to focus more on the quality of outreach when other factors are inplace. This result, once more, supports the views of those who are unworried aboutthe well-familiar mission drift concern.

Depth of outreach: Goodbye to female credit clients? The share of loansextended to women clients is another reliable indicator for measuring MFIs’ depthof outreach. By extending loans to women clients MFIs generally meet their pov-erty-fighting and empowerment objectives as women are usually perceived poorerthan their male partners and less autonomous at any given level of wealth orincome. So, a higher percentage of women borrowers will signal a better-qualityoutreach to the poor from the MFI perspective. In that sense, it is quite logical toexpect that MFIs are to place more weight on women credit clients to avoid theconcerns for mission drift.

Table 7 authenticates the findings already obtained in Tables 5 and 6. The coef-ficients for the capital structure variable CAR are all equally negatively signed andDER has the reassuring positive signs. However, these coefficients are always insig-nificant. The loans-to-assets-ratio variable has been negatively signed and its coeffi-cient is significant only in model 1, confirming significant increases in femaleparticipation with a higher focus on lending. Coefficients for both of the risk vari-ables – Z-risk and PAR30 – are now positively signed but they are insignificant.These suggest that the preference of MFIs for female customers when the repay-ment risk is higher has no statistical basis and this is against the female clients’established loan-repayment reputation. Similar explanations also apply to the Z-riskvariable and its significance as a lower Z-risk implies higher probability of default.The size of the MFI is found to be negatively related with this female dimension ofdepth of outreach variable and is dissimilar to other outreach regressions’ results.This negative significant relationship confirms that larger MFIs are found to focusless on female customers – a case for mission drift. The coefficients for the age var-iable are found to be positive all the way through and highly significant in model1. This substantiates the fact that as MFIs grow older they prefer to stick withfemale loan clients.


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4. Conclusions

Agency theory suggests that relatively high leverage raises the expected costs offinancial distress, bankruptcy or liquidation. Thus, the agency costs of outside debtoverpower the agency costs of outside equity, so further increases in leverage resultin higher total agency costs. This study explores whether such agency costs hypoth-esis works in terms of MFIs’ performance. We use profit-efficiency and cost-effi-ciency as indicators of MFIs’ financial performance. In order to measure outreachperformance of MFIs we utilized both depth and breadth dimensions that again vali-date the appropriateness and sufficiency of MFIs’ targeting criteria. Results confirmthe agency theoretic claim that an increase in leverage – i.e. a reduction in capital-assets-ratio – indeed raises profit-efficiency. In addition, cost-efficiency deteriorateswith increasing capital-assets-ratio (i.e. with decreasing leverage). Other resultsobtained in this exercise are also commendable. Employing a different sample andchecking robustness with numerous estimation techniques did not bring any mate-rial changes to the regression results.

The study is based on a large number of MFIs, which operate, unlike other busi-ness entities, on double bottom-line principles. They have a social mission to reachthe poor and they are to accomplish a number of financial targets as well. So, thepremise we started working on was different and our results in a number ofinstances are dissimilar to the already-existing mixed empirical evidences, althoughmostly the results are consistent with the agency costs hypothesis. Apart from the

Table 7. Depth of outreach regressions (dependent variable: log of (percentage of) womenborrowers).

FE2SLS GMM-FE LIML-FE

CAR �0.070 �0.123 �0.077(0.146) (0.179) (0.212)

DER 0.264 0.245 0.287(0.738) (0.891) (1.063)

Loans �0.233⁄ �0.276 �0.232(0.100) (0.158) (0.192)

Log Z-risk 0.067 0.087 0.069(0.054) (0.071) (0.083)

PAR30 0.921 1.459 0.986(0.941) (1.173) (1.560)

Size �0.064⁄ �0.067 �0.065(0.031) (0.043) (0.044)

Log age 0.266⁄⁄⁄ 0.271 0.266(0.081) (0.139) (0.145)

Constant 4.454⁄⁄⁄(0.364)

Hansen’s J-test 0.89 0.89Hausman w2 26.87P-values 0.0004N 1675 1553 1553

Notes: Coefficients for the ‘regulated’ and regional dummies are not presented. Hausman’s tests for(FE2SLS – ES2SLS) are given in w2 values. HAC standard errors are in the parentheses. ⁄Statisticallysignificant at the ⁄10%, ⁄⁄5% and ⁄⁄⁄1% levels.

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uniqueness of MFIs, another likely reason for this is possibly the specification ofprofit-efficiency as an inverse measure of agency costs. In addition, issues such aseconomies of scale and interest rate sensitivity in microfinance still remain largelyunexplored and the subject should be studied more rigorously. We hope to be ableto address these issues in future works.

AcknowledgementsThe author would like to thank two anonymous referees for very helpful comments. Anyremaining errors are, of course, the author’s.

Notes1. Although MFIs are specialized financial institutions with social and financial missions,

similar definitions of financing and capital structure should be used for them.2. The institutionists stress the importance of profit-orientation, efficiency and self-sustain-

ability in microfinance operations so that MFIs can generate enough revenue to meettheir operating and financing costs. They argue that MFIs’ may attain these goals throughmassive scaling up that ultimately make them resilient to increased competition. In con-trast, welfarists are less interested in MFIs’ efficiency and self-reliance. They emphasizeself-employment and immediate improvement in the well-being of poor clients – espe-cially women.

3. Modigliani and Miller (1958) provides numerous propositions to develop the theoreticalfoundations of the crucial link between capital structure and firm value, which is stillbelieved to be relevant given that several strong assumptions (such as the existence ofperfect capital market, homogeneous expectations, absence of taxes and zero transactioncost) are needed for it to hold. It held the position that capital structure is irrelevant andhas basically no impact on firm performance. However, although a few also supportedtheir views, this theory was later challenged by others as corporate governance theoryserved to understand the complex financing behaviour of corporate bodies (for detaileddiscussions see, for instance, Cassar and Holmes 2003; Kyereboah-Coleman 2007).

4. For brevity’s sake, basically, we skipped detailed discussions on relevant literature.Among others, for example, Berger and Bonaccorsi di Patti (2006), Kyereboah-Coleman(2007) and Cassar and Homes (2003) provide elaborate details on previous works.

5. The available MFI profiles and data are available in the public domain: www.mixmarket.org.

6. The Mix Market classifies MFIs in accordance with the level of information disclosure(within 1–5) provided. The higher the level provided the better the quality of data.

7. In our sample, several observations for ROE and Z-risk scores have been excludedbecause of their negative or zero values. MFIs with negative debt-equity ratios were alsoeliminated to avoid bad leverage. Some of the variables including DER, ROE, Z-riskand PAR30, for example, have been transformed through adding or multiplying a posi-tive constant to every MFI’s observation so that the natural log is taken on a positivenumber. Natural logarithms have been taken so that normally distributed residuals areobtained and that the t-tests are valid.

8. The developing regions, according to the World Bank classification, are: East Asia andthe Pacific (EAP), Eastern Europe and Central Asia (EECA), Latin America and theCaribbean (LAC), Middle East and North Africa (MENA), South Asia (SA) and Sub-Saharan Africa (SSA). Results of these time-constant dummies could not be reportedsince we have finally chosen the FE models.

ReferencesArmendaritz de Aghion, B., and J. Morduch. 2005. The economics of microfinance. London:

MIT Press.Baltagi, B.H. 1981. Simultaneous equations with error components. Journal of Econometrics

17, no. 2: 189–200.


Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

10:

54 1

1 N

ovem

ber

2014

Baltagi, B.H. 2006. Estimating an economic model of crime using panel data from NorthCarolina. Journal of Applied Econometrics 21: 543–7.

Baltagi, B.H. 2008. Econometric analysis of panel data. New York: Wiley.Berger, A.N., and E. Bonaccorsi di Patti. 2006. Capital structure and firm performance. A

new approach to testing agency theory and an application to the banking industry.Journal of Banking and Finance 30: 1065–102.

Cassar, G., and S. Holmes. 2003. Capital structure and financing of SMEs: Australianevidence. Accounting and Finance 43, no. 2: 123–47.

Christen, R.P., R. Rosenberg, and V. Jayadeva. 2004. Financial Institutions with a ‘DoubleBottom Line’: Implications for the future of microfinance. Occasional Paper No. 8.Washington, DC: CGAP.

Cull, R., A. Demirguc-Kunt, and J. Morduch. 2007. Financial performance and outreach: Aglobal analysis of leading microbanks. The Economic Journal 117: F107–33.

Cull, R., A. Demirguc-Kunt, and J. Morduch. 2009. Microfinance meets the market. Journalof Economic Perspectives 23, no. 1: 167–92.

Deaton, A. 1995. Data and econometric tools for development analysis. In Handbook ofdevelopment economics volume 3A of handbooks in economics, ed. Jere BehrmanSriniva-san,, T.N., 1785–1882. Amsterdam: Elsevier.

Fama, E.F., and K.R. French. 2002. Testing trade-off and pecking order predictions aboutdividends and debt. Review of Financial Studies 15, no. 1: 1–33.

Hahn, J., J. Hausman, and G. Kuersteiner. 2004. Estimation with weak instruments:Accuracy of higher-order bias and MSE approximations. Econometrics Journal 7, no. 1:272–306.

Hannan, T.H., and G.A. Hanweck. 1988. Bank insolvency risk and the market for largecertificates of deposit. Journal of Money, Credit and Banking 20, no. 2: 203–11.

Hansen, L.P. 1982. Large sample properties of generalized method of moments estimators.Econometrica 50: 1029–54.

Hartarska, V., and D. Nadolnyak. 2007. Do regulated microfinance institutions achieve bettersustainability and outreach? Cross-country evidence Applied Economics 39, no. 10–12:1207–22.

Hartarska, V. 2005. Governance and performance of microfinance institutions in central andeastern Europe and the newly independent states. World Development 33: 1627–48.

Helms, B. 2006. Access for all. Washington, DC: CGAP.Kyereboah-Coleman, A. 2007. The impact of capital structure on the performance of micro-

finance institutions. The Journal of Risk Finance 8: 56–71.Mersland, R., and O. Strom. 2009. Performance and governance in microfinance institutions.

Journal of Banking and Finance 33: 662–9.Modigliani, F., and M. Miller. 1958. The cost of capital, corporate finance and the theory of

investment. American Economic Review 48: 261–97.Morduch, J. 2000. The microfinance schism. World Development 28: 617–29.Navajas, S., M. Schreiner, R. Meyer, C. Gonzalez-Vega, and J. Rodriguez-Meza. 2000.

Microcredit and the poorest of the poor: Theory and evidence from Bolivia. WorldDevelopment 28: 333–46.

Schreiner, M. 2002. Aspects of outreach: A framework for discussion of the social benefitsof microfinance. Journal of International Development 14: 591–603.

Woller, G., C. Dunford, and W. Woodworth. 1999. Where to microfinance? InternationalJournal of Economic Development 1: 29–64.

Wooldridge, J.M. 2002. Econometric analysis of cross section and panel data. Cambridge,MA: The MIT Press.

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does capital and financing structure have any relevance to the performance of microfinance...

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