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A cross-country analysis to investigate

the true role of microfinance

institutions in developed and

developing economiesMuhammad Sajid Saeed

a

aDepartment of Accountancy, Finance and Risk, Glasgow

Caledonian University, Cowcaddens Rd, Glasgow, Lanarkshire G4

0BA, UK

Published online: 06 Mar 2014.

To cite this article:Muhammad Sajid Saeed (2014): A cross-country analysis to investigate the true

role of microfinance institutions in developed and developing economies, Journal of SustainableFinance & Investment, DOI: 10.1080/20430795.2014.883301

To link to this article: http://dx.doi.org/10.1080/20430795.2014.883301

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Zeller and Meyer (2002) also doubt about the actual role of MFIs due to a number of issues ham-

pering the proper implementation and growth of micronance in developing and developed

countries.

2. Literature review

The role of MFIs on alleviating the poverty has gained momentum. Different opinions are found

in this context. Some believe that MFIs play a positive role in reducing poverty while others

perceive that they signicantly contributed to the nancial crisis of 2008 (Srnec, Vyborna, and

Havrland2009). But before discussing the role of MFIs in detail, it is essential to understand

the difference between different types of MFIs to evaluate the nature of nancial services

offered by each of them.

Normally, four types of government and non-government institutions are formally involved in

micronance activities. These intermediaries could be commercial banks, specialised nancial

institutions, state-owned banks and nance organisation. Recently, many commercial banks in

both developing and developed economies have started to inltrate into the micronancesector. There are several ways that commercial banks can engage in micronance activities.

For instance, they can either directly interact with borrowers or indirectly involve by generating

funds. In general, commercial banks participate in micronance activities in four ways: direct

lending, partnership with MFIs, micronance subsidiary, or securitisation (Ledgerwood 1999;

Rhyne2009).

The direct lending ability of commercial banks allows them to serve the micronance sector

without any issue or delay. The Grameen Bank started group lending in 1976 where the loan was

attributed to each individual in a group. However, if in case any individual defaults his/her current

credit than he/she may not get the approval of new loan. The group-lending procedure involves a

responsibility of borrowers to reimburse their credits on time and in a disciplined way(Ledgerwood1999). Commercial banks also establish a partnership with MFIs and lend them

in a variety of ways such as retail and wholesale banking. On the other hand, MFIs collect,

monitor, and originate loans. Indeed, MFIs obtain many benets working with commercial

banks. As the greater capital can enlarge loan size, so the banks may introduce their products

and services to other geographical areas (Rhyne 2009). One such example is the ICICI Bank

in India that has established alliances with more than 72 MFIs throughout the country and

looking to raise the number of partnerships to 250 by the end of 2013 (Ugur2006).

Another signicant practice of commercial banks for starting micronance operations is to

establish new subsidiaries. These subsidiaries help commercial banks to alleviate the level of

risk while lending to low-income people. From the point of view of borrowers, dedicated micro-nance services offered by the commercial banks may develop high trust and indicate banks

commitment to poverty reduction. Finally, commercial banks also play a signicant role in the

context of micronance by generating capital in local and international markets to support and

strengthen the operations of MFIs (Ledgerwood1999).

3. Research objective and contribution

In the past, the role of very few MFIs has been explored so far, and existing studies do not clearly

reveal any conclusive results showing the performance of MFIs in developing and capitalist econ-

omies (Momoh2005; Ltzenkirchen2012). Therefore, to date, the role of MFIs is rather unclear

and the reason is the lack of comparative studies that compare and analyse the role of MFIs in

reducing the poverty in both developing and developed countries. Therefore, this research signi-

cantly contributes to the nance literature by investigating the actual and perceived role of MFIs

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in reducing the worldwide poverty level. For this purpose, a cross-country analysis is conducted

by considering the cases of different MFIs operating in different countries such as Bangladesh,

India, Pakistan, the UK, and USA.

4. Research design and methodologyThis research is based on the exploratory research design due to its non-experimental nature and

logical use of qualitative approach. Another reason of using the exploratory research design is to

address the strong need of exploring the role of MFIs which is still unclear due to lack of studies in

this domain. The study does not rely on any hypothesis and also does not employ large data

samples. It is based on existing data available about MFIs for cross-country analysis. The quali-

tative approach is followed in this research on the grounds of its nature and core research aim

which is to effectively measure the efciency and efcacy of MFIs through qualitative data avail-

able in the form of facts and gures regarding MFIs. In addition, empirical data about various

MFIs of selected countries also allow the researcher to conduct cross-country analysis.

4.1. Population

The population of this research mainly consists of formal MFIs operating in developed and

developing countries. These countries include: Asian countries including Bangladesh, India,

and Pakistan), the UK, and the USA. All MFIs operating in these countries are chosen to

assess their true role in reducing poverty.

4.2. Data collection and analysis

The work in this study is primarily based on meaningful secondary data. Therefore, it is collected

from many reliable sources which include websites, document reviews, micronance case studies,commercial banks, MFIs, journals and books, and various Internet sources. The key sources of

acquiring processed information about micronance are the MFIs operating in Asia, the UK,

and USA regions. In order to support qualitative reasoning during the cross-country analysis,

the empirical data from 2006 to 2011 about MFIs are collected from http://www.mixmarket.

orgdatabase. The empirical data for 2012 are not collected because it was not fully updated in

micronance databases. The data collected about MFIs are based on 10 micronance indicators

suggested by Becker (2010). These indicators are micronance borrowings, gross loan portfolio,

number of active borrowers, return on asset (ROA) ratio, return on equity (ROE) ratio, deposit-to-

loan (DTL) ratio, average loan balance per borrower, cost per borrower, and cost per loan. The

average of each indicator for all MFIs associated with each country is taken on a yearly basis.Data analysis is the backbone of this research. Each countrys MFIs are analysed and evalu-

ated on the basis of empirical information that indicates the effectiveness and productivity of those

institutes in reaching and serving low-income people. The average of each micronance indicator

for all MFIs associated with each country is taken on annual basis. The reliability and validity of

the data are analysed through Cronbachs alpha (C) test using Statistical Package Social Sciences

(SPSS) application. The results of C test are illustrated in Table 1where all 10 indicators are cate-

gorised into four groups. Each groups Cindicates satisfactory results. The table also shows that

the overall reliability of the data is 0.825 which is considered as good.

Furthermore, Karl Pearsons correlation coefcient is used to nd correlations between the

micronance indicators of each country. The value of correlation coefcient (r) should remain

between

1 and +1 where answer near to

1 represents negative relationship between variables

and near to +1 indicates a positive relationship (Black 2009). The graphs and tables are

constructed in MS Excel to represent empirical data.

Journal of Sustainable Finance & Investment 3
http://www.mixmarket.org/http://www.mixmarket.org/http://www.mixmarket.org/http://www.mixmarket.org/


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5. Cross-country analysis

5.1. Analysing gross loan portfolio

A gross loan portfolio represents all outstanding principal amounts that are due for entire

outstanding client loans. Apart from interest receivable, this undertakes all renegotiated loans,

delinquent, and current loans. Figure 1 represents the outstanding amounts of MFIs operating

in selected developed and developing countries where highergures indicate more outstanding

principals. An exceptional case is evident in the gure which shows that the USA has provided

a greater number of microloans to low-income people between 2008 and 2009 to stimulate the

economy in order to avoid the adverse impacts of the recession.

On the other hand, Bangladesh is comparatively ahead of other countries in providing micro-

loans to poorer people. A continuous increasing trend from 2006 to 2008 in Bangladeshs gross

loan portfolio represents the emergence of various new MFIs in the micronance sector (Delimat-sis and Herger2011). Apart from the developed countries such as the UK and USA, the trend line

of gross loan portfolio of developing countries looks stable throughout the period. This could be

the reason that micronance facility was not or partly available in these countries before the 2008

recession. As compared to the Bangladesh and other developing countries, most of the

Table 1. Reliability analysis.

Category Indicator Items C Result

Gross loan portfolio Gross loan portfolio 1 0.722 AcceptableBorrowing related Borrowings 4 0.901 Excellent

Number of active borrowersAverage loan balance

Financial stability related ROA ratio 3 0.841 GoodROE ratioDTL ratio

Cost related Cost per borrower 2 0.754 AcceptableCost per loan

Overall reliability 10 0.825 Good

Figure 1. Gross loan portfolio (000).

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The correlation matrix inTable 3provides somehow similar results asTable 1. The borrow-

ings level in India is positively correlated with those of in the USA and UK.Table 2also illustrates

a strong correlation between the UK and USA at the 0.05 signicance level. On the other hand,

the correlation between Bangladesh and UK is positive, but it is insignicant at 0.01 and 0.05

levels due to differences in the demand of micronance in both countries.

5.2.2. Number of active borrowers

The number of active borrowers means individuals or groups who presently have an outstanding

loan balance. This also refers outstanding balances with MFIs that people are responsible to repay

on time in the proportion of the gross loan portfolio. However, a person having more than one

loan is considered as a single borrower. As shown inFigure 3Bangladesh leads other countries

in having the maximum number of borrowers each year from 2006 to 2011. India due to its exten-

sive poverty level is at the second position followed by Pakistan. The USA compared to the UK

has a higher number of active borrowers because of the public awareness and improvement in the

knowledge of micronance (Olsen2010).The correlation matrix inTable 4demonstrates signicant positive relationships among the

UK, USA, and India, and negative relationship between Bangladesh and other countries in

terms of number of active borrowers throughout the chosen period. This shows the parallel

increasing and decreasing trends in these countries within the same period.

Table 3. Correlation matrix.

BNG IND PK USA UK

BNG 1IND 0.151 (0.776) 1

PK

0.156 (0.768)

0.364 (0.478) 1USA 0.142 (0.788) 0.872* (0.023) 0.176 (0.739) 1UK 0.034 (0.948) 0.821* (0.045) 0.247 (0.637) 0.967** (0.002) 1

Note: BNG, Bangladesh; IND, India; PK, Pakistan.** Represent 0.01 signicance level.* Represents 0.05 signicance level.

Figure 3. Number of active borrowers (000).

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5.2.3. Average loan balance per borrower

The average loan balance per borrower is computed as dividing the gross loan portfolio by the

number of active borrowers. It is quite amusing that both the UK and USA have the highest

average per borrower. This may be because of the difference of exchange rates between developed

and developing countries. For example, giving 300 microcredit can be a small amount for a UKnational but it worth as medium loan for the people living in Asian developing countries. This is

also the reason that the UK has a high average loan balance per borrower compared to Bangladesh

as shown inFigure 4.

The correlation matrix inTable 5gives that positive correlation among Bangladesh, India, and

the USA at the 0.01 signicance level. This means the ratio of average loan balance per borrower

is somehow similar among these three countries. On the other hand, the table reveals statistically

insignicant but positive correlations between Pakistan and India and the UK and USA.

5.3. Analysingnancial stability

5.3.1. ROA ratio

The ROA ratio is expressed as deducting taxes from net operating income and dividing it by total

assets. The high ROA ratio is better because it indicates the efcient use of assets in generating


BNG IND PK USA UK

BNG 1IND 0.052 (0.922) 1

PK

0.206 (0.695)

0.034 (0.949) 1USA 0.421 (0.406) 0.783 (0.066) 0.300 (0.563) 1UK 0.504** (0.001) 0.841* (0.036) 0.259 (0.620) 0.881* (0.020) 1

Note: BNG, Bangladesh; IND, India; PK, Pakistan.** Represent 0.01 signicance level.* Represents 0.05 signicance level.

Figure 4. Average loan balance per borrower.



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funds.Figure 5illustrates interesting results where the ROA ratio of UK MFIs was more assets

intensive and consequently negative throughout 20062011 except 2008 when it increased 200%

due to changes in nancial policies to mitigate the impact of the recession (Imai et al. 2011).

Bangladesh performed well in keeping the balance between assets and earnings and therefore

has a higher ROA compared to other selected countries. India and the USA also achieved positive

ROA, whereas Pakistan faced negative ROA from 2006 to 2010. The positive gure in 2011 indi-

cates better performance in terms of utilising best use of its assets.

The correlation matrix inTable 6indicates only one positive correlation between the USA andIndia which is signicant at the 0.01 level. All other values are not statistically signicant with

each other, but they do have positive or negative associations.

Figure 5. ROA ratio.


BNG IND PK USA UK

BNG 1IND 0.846* (0.034) 1

PK

0.057 (0.915) 0.335 (0.516) 1USA 0.911* (0.012) 0.800 (0.056) 0.184 (0.728) 1UK 0.259* (0.011) 0.090 (0.865) 0.063 (0.906) 0.366 (0.476) 1

Note: BNG, Bangladesh; IND, India; PK, Pakistan.* Represents 0.05 signicance level.


BNG IND PK USA UK

BNG 1IND 0.109 (0.837) 1PK 0.467 (0.350) 0.026 (0.962) 1USA 0.038 (0.943) 0.329* (0.024) 0.459 (0.360) 1

UK

0.381 (0.457) 0.567 (0.240)

0.227 (0.666) 0.344 (0.504) 1


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5.3.2. ROE ratio

The ROE ratio is calculated by deducting taxes from net operating income and divided it by share-

holders equity. In fact, ROE measures MFIs efciency of generating funds from each unit of

shareholders equity. InFigure 6negative gures of UK MFIs are not the good sign of perform-

ance which often causes negative income consequences such as shareholders may withdraw

remaining nances from the business. Although Bangladesh, Pakistan, and India faced several

ups and downs in ROE due to economic and nancial considerations, but they were able to main-

tain positive values throughout the period.Table 7of correlation matrix indicates only one strong

correlation value that is 0.832 between the USA and UK which is also statistically signicant at

the 0.01 level. Other countrys MFIs are insignicantly correlated with each other which demon-

strate their different ROEs in different periods.

5.3.3. DTL ratio

The DTL ratio is calculated as dividing deposits by gross loan portfolio. Here, deposits represent

all compulsory, voluntary, institutional, or retail deposits whereas a gross loan portfolio is the out-

standing client loans. InFigure 7, the higher percentages indicate that deposits more than ade-

quately funded the loan portfolio. In this regard, again Bangladesh clean sweeps othercountries with high DTL ratios. Interestingly, the second highest but unstable ratios are achieved

by Pakistan and the USA which is a good sign of micronance success in these countries. On the

Figure 6. ROE ratio.


BNG IND PK USA UK

BNG 1IND 0.418 (0.409) 1PK 0.567 (0.240) 0.120 (0.821) 1

USA 0.096 (0.856) 0.534 (0.275) 0.047 (0.929) 1

UK 0.011 (0.983)

0.085 (0.873) 0.177 (0.737) 0.832* (0.040) 1




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other hand, India and the UK have achieved lower DTL ratios throughout the period. Bangladesh

maintains high percentages compared to other countries because the country introduced micro-

nance activities several years ago.

InTable 8, the correlation matrix reveals only two statistically signicant correlations such as

0.259 and 0.146 that are between India and the UK, and USA and Pakistan. These correlations

exist because of the stability in the percentages of DTL ratio among these countries.

5.4. Analysing costs

5.4.1. Cost per borrower

Cost per borrower is expressed as dividing operating expenses by the number of active bor-

rowers within a particular time period. It primarily gives an idea of the average cost of maintain-

ing a single current borrower. Figure 8 indicates the cost per borrower for each country in

providing micronance services to poor clients. It is evident in Figure 8 that Bangladesh has

the lowest cost per borrower followed by India whereas the UK has the highest cost per bor-

rower. This is because of the currency and exchange rate differences as the UK has the

highest currency value among all selected countries; or in other words, the value of Bangladesh,

India, and Pakistan currencies (for example, Taka and Rupees) are very low as compared to the

Figure 7. DTL ratio (000).


BNG IND PK USA UK

BNG 1IND 0.314 (0.544) 1PK 0.710 (0.114) 0.239 (0.648) 1

USA

0.047 (0.929)

0.303 (0.559) 0.146* (0.013) 1UK 0.864* (0.026) 0.259* (0.015) 0.599 (0.209) 0.337 (0.514) 1


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UK. But it is amusing to see that Pakistan has the second highest cost per borrower, even more

than the USA. This could be the result of lack of knowledge and awareness of micronance in

the country. The correlation matrix of cost per borrower in Table 9demonstrates a negative cor-

relation between the UK and other countries due to the fact of huge differences between the

costs per borrower.

5.4.2. Cost per loan

Similar to cost per borrower, cost per loan gives an idea of the average cost of maintaining a

single loan. It is expressed as operating expenses by the average number of outstanding loans

within a particular time period. Figure 9 reveals quite similar results as Figure 8 where

Bangladesh and India have the lowest cost per loan compared to other countries. On the

contrary, the UK and Pakistan have a high-cost per loan. Unfortunately, this is the permanent

drawback of high-value currencies but the impact of this drawback could be reduced by concen-

trating on increasing ROA and DTL ratios by stimulating gross loan portfolio and customer

deposits (Harper and Arora2005).Table 10also reveals a negative but statistically signicant

relationship between the UK and Bangladesh MFIs. In addition, a positive correlation

between India and Bangladesh MFIs illustrates the stability and continuous low costs of both

countries within the selected period.

Figure 8. Cost per borrower (000).


BNG IND PK USA UK

BNG 1IND 0.522 (0.288) 1PK 0.381 (0.457) 0.754 (0.084) 1

USA 0.389 (0.446)

0.079 (0.882) 0.050 (0.925) 1UK 0.954** (0.003) 0.381 (0.456) 0.164 (0.757) 0.287 (0.581) 1

Note: BNG, Bangladesh; IND, India; PK, Pakistan.** Represent 0.01 signicance level.



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Figure 9. Cost per loan (000).


BNG IND PK USA UK

BNG 1IND 0.589* (0.019) 1PK 0.458 (0.361) 0.467 (0.350) 1

USA 0.607 (0.201) 0.276 (0.597) 0.597 (0.211) 1UK 0.793* (0.027) 0.556 (0.252) 0.277 (0.595) 0.614 (0.195) 1


Figure 10. Average borrowings.

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6. Conclusions

The cross-country comparison reveals some interesting insights about micronance success/

failure in Asian developing and the two worlds developed countries such as the UK and USA.

Apart from some exceptional cases, the overall analysis reveals that Bangladesh and India are

comparatively ahead in the success rate of micronance implementation among all countriestaken as case studies. The averages of all indicators from 2006 to 2011 are taken in

Figures 1015. The average of borrowings in Figure 10illustrates the strong positions of India

Figure 11. Average gross loan portfolio.

Figure 12. Average number of active borrowers.



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and Bangladesh in acquiring borrowings to facilitate micronance activities. Interestingly, the

average gross loan portfolio of the USA followed by Bangladesh is evident inFigure 11which

is a good sign for micronance implementation in both developed and developing countries.

The strong position of Bangladesh is clearly demonstrated in Figures 12 and 13 where

Bangladesh dominates other countries in terms of number of active borrowers and average

loan balance per borrower. The positions of the UK and USA are inadequate at this moment

because the micronance activities are relatively new in these countries. In fact, this conceptbecame popular after the nancial crisis of 2008 when governments started to intensify such

activities to stimulate their economies.

Figure 13. Average loan balance per borrower.

Figure 14. Averagenancial stability (20062011).

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Figures 14and15also demonstrate dominating positions of Bangladesh and India in terms of

performance (measured through ratios) and cost of loans and individual borrowers. The

micronance performance in the UK in terms of ratio analysis is not satisfactory compared to

other countries.

It is also concluded on the basis micronance indicators that MFIs in the UK and USA lack of

performance, nancial stability, outreach, and cost compared to the developing countries

particularly Bangladesh and India. This is because that the micronance concept is new in

these developed countries and hence there is a strong need to intensify micronance activities

to increase awareness and knowledge.

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Black, K. 2009.Business Statistics: Contemporary Decision Making. 6th ed. Hoboken, NJ: John Wiley and

Sons.Delimatsis, P., and N. Herger. 2011. Financial Regulation at the Crossroads: Implications for Supervision,

Institutional Design and Trade. Alphen aan den Rijn: Kluwer Law International.Gov.UK. 2013. Benets: Includes Tax Credits, Eligibility and Appeals.www.gov.uk/browse/benetsHarper, M., and S. S. Arora. 2005. Small Customers, Big Market: Commercial Banks in Micronance. New

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