Current Outreach Of The Microfinance Industry

Published Date: 02 Nov 2017

Since the inception in the 1970’s microfinance has grown immensely. According to the microfinance summit state campaign report in 2007, microfinance programs reached to 100 million of world’s poorest families and over 150 million clients were served. While in 2005 more than 100 million people received microloans and in 2007 100 million poorest people mark were reached. In addition to this 83 percent of the poorest clients were women. The mentioned outreach figure consist of those microcredit organizations which reports to the microcredit summit organization. The growth of microfinance reported by microcredit summit organization is shown in figure bellow, which shows the result of new microfinance institutions and report for the first time to the summit. Other than that figure shows that microfinance is reached to a significant number of people and especially poor people. To give a better idea of the scale of outreach, according to World Bank in 2005 there were somewhat 576 million people living under the poverty line of US 1 dollar (Chen and Ravallion 2008).

Figure below also shows the growth of the depth of outreach. The target poor clients have risen from 56 percent in 1997 to 69 percent ten years later. The share went to the highest point in 2005 which was 72 percent, after there has been seen a decline in the poor clients share. This can be interpreted in both ways i.e. it may be due to normal annual fluctuation or it may be due to some microfinance institutions going towards the upper segment of clients spectrum.

Table

Tradeoff between efficiency and outreach

Since the formal introduction of microcredit in the late 1970’s, it has been called as a major poverty alleviation model in the developing countries. Where its positive impacts have been praised by lifting millions of poor out of poverty, it has also been criticized by many practitioners and academics. After examining the debate on microfinance as a poverty reduction model, still it is not clear whether microfinance contributes to the reduction of poverty and whether microfinance is an efficient method to reduce poverty (Hermes and Lensink, 2007). Lucarelli (2005) says that more careful approach should be taken, and warns that microcredit does play an important role in the development process and overcoming poverty, but microfinance should not be too much relied to cure the complex development problems. Muhammad Yunus (pioneers of the microfinance model) emphasis that microfinance model is not a miracle cure that will eliminate poverty in one sweep. He further said that for many it will end poverty and for many others poverty will be reduced. When microcredit is combined with other innovative programs it will act as an essential tool for poverty free world (Yunus and Jolis 1999:171).

In order the model to be truly effective, the services offered by microfinance institutions have to reach to those segments of the society which lie at the bottom of the pyramid. Despite the universal acceptance and recognition microfinance products and services need greater flexibility, so far there hasn’t been any such innovation that address poor people needs on a large scale, and the outreach of microfinance program is quite lower than what actually is needed so to lift the very poor (Barua and Sulaiman 2006).

Most of the discussions argue about outreach that there is a tradeoff between depth of program outreach and institutional sustainability, if microfinance institutions focus on achieving depth of outreach than they have to sacrifice breadth of outreach, as it is more costly and difficult to reach and generates revenues from poor. Therefore Lending credit to the poor is not considered financially viable, it’s because of higher processing cost and little income generation while serving poor, and moreover poor do not have good credit history and are prone to default (Pischke 1991; Tyhs 2000; Churchill, Hirschland et al. 2002; Ivatury 2005). One the other hand Maes and Foose (2006) claims that despite of the high transaction cost, high risk and other challenges, a number of microfinance institutions, nongovernmental organizations and multilateral agencies are specifically providing microfinance services to the very poor, while there are other microfinance programs that are not reaching the very poor but are interested in finding new approaches.

How can the extremely poor people be reached? Matin and Hulme (2002) have forwarded three ways of making microfinance services to be more poverty focused i.e. identifying and reaching the potential poor, attracting the poor and excluding or discouraging the non poor. And above all of these, visionary leadership and organizational commitment is a fundamental driving force to achieve greater depth of outreach (see Hulme and Mosley 1996; Johnson and Rogaly 1997). If the management is strongly committed with the mission that of reaching the very poor even if takes foregoing revenue, and around this objective the organizational procedures will be designed and implemented. Maes and Foose (2006) says that the commitment of top management is essential, and this top management commitment need to be accompanied by the overall institution culture so to provide continue microfinance services to very poor people. Staff incentives can be introduced i.e. if staff is satisfied and motivated than they will be more loyal to their work and will help microfinance institution to achieve its targets and mission, they will be more focused while selecting clients. Apart from these discussed measures, simplified and decentralized branch level operations can help in cost reduction and can also help in encouraging the very poor to join such programs, by making products that are approachable and affordable to clients.

Achieving self sufficiency for a microfinance institution is not that easy, and the high spread of subsidies in the microfinance industry shows that. The high dependency on subsidies is not surprising, and even with the innovations regards to lending methods and achievements so to narrow down the organizational forms, still it remains a fact that lending small amount of loans to many people is far more costly than to lend large amount to few people. Other than that microfinance institutions extend credit products and services to rural and poor areas, which is another factor for microfinance institutions that increases operating cost. So in order to make microfinance institution financial self sufficient it has to increase its income yield or reduce it operational expense or loan losses (Christen 1997; Morduch 1999b).

Microfinance Institutions can reduce the operational costs and loan losses by doing more efficient operations. Christen (1997) says that in the prior years the high profits in the microfinance industry have been made by decreasing costs and operating losses. After these learning costs and start up costs are incurred and the microfinance institution matures than they are in position to streamline their operations (like their loan appraisal techniques) and secure high repayment rates.

ASA in Bangladesh is an example of successful streamlining, which has developed simpler and easy ways to manage the loan contracts and also had slimmed down the administration; emphasis had been on less educated workers, so to reduce labor costs significantly (Morduch 1999b). Cull et al. (2007) using a regression analysis approach finds out that containment of cost has an important role in determining the microfinance institution sustainability, regardless of the lending methodology i.e. individual or group lending or village banking.

Empirical analysis: Studying the Efficiency and Outreach in Pakistan

Hypothesis

This thesis uses microfinance institution in Pakistan as a sample to study the potential patterns of efficiency and outreach. The aim is to test whether there is any relationship between efficiency and outreach within the microfinance sector.

Commercialization of microfinance institutions may result in an increase in efficiency due to the increased competition for micro loans in the market. However, at the same time commercialization and increase in efficiency involve many risks. Most importantly, when there is increase in efficiency it may reduce the scope for lending to the poor. Other than that increasing competition give benefit to wealthier borrowers from increasing competition among microfinance institutions, which will lead to lower level of welfare for the poorer borrowers.

Having said that, the higher levels of profitability of microfinance institutions is related with larger average loan sizes, which indicates that institutions may experience a tradeoff between the efficiency and outreach and are therefore suspect to mission drift. When institutions get matures it is also consider occurring of mission drift, and therefore older and larger institutions should also demonstrate higher average loan sizes, similar patterns relates to the share of women borrowers as well.

Several authors argue that there is a tradeoff between the depth of outreach and financial sustainability since transaction cost of smaller loans are high as compared to transaction cost of larger loans. Therefore when there is a shift of small microfinance institutions to large providers of banking services may go against the microfinance institutions traditional aim i.e. to provide credit to the poor.

Introduction to Pakistan

Before turning to the analysis, it is useful to present some basic facts about the poverty situation and the financial sector of Pakistan in order to put the ensuing analysis into context.

Pakistan is a rapidly developing country and a major emerging market, with 7 percent per annum of economic growth rate for four consecutive years up to 2007 (Pakistan Times, 2007). Pakistan has a total population of 160 million people of which people living in rural areas are 65 percent. It has been relative outlier in the region, and had ranked low on both, Human Development Index (HDI) and domestic product per capita. Due to internal political disputes Pakistan has suffered a lot, due to this country had low levels of foreign investment, other than that the confrontation with neighboring country India had been costly. However after the macroeconomic reforms in 1990’s are now resulting in positive results and had been helpful in transforming Pakistan into a resurgent and stable economy.

Inflation remained the biggest threat to the Pakistan economy. In 2005 it jumped more than 9 percent and was due to the rising commodity and oil prices which put strong domestic demand and supply price shocks. State Bank of Pakistan in these circumstances tightened the monetary policy and has managed to bring the inflation down to 7.5 percent in year 2006. While in October 2007 inflation again went up and stood at 9.3 percent, reason for this upward trend was due to the sharp pickup in food inflation. Tight monetary policy from State Bank of Pakistan had mainly helped in the stability in core inflation (Investors Relations Desk, 2007).

Poverty profile of Pakistan

Despite the positive developments in macroeconomic in recent years, still there is a widespread poverty in the country, which is the core problem. There was rise in poverty during 1990’s, in response to that Government of Pakistan launched poverty reduction strategy papers in 2001 and 2003. The poverty reduction strategy main focus was on accelerating the economic growth and maintaining the macroeconomic stability by augmenting targeted interventions, investing in human capital, social safety nets expansion and governance improvement (CGAP, 2007).

According to Government of Pakistan population censes organization (GoP 2011), in February 2011 the estimated population was at 175 million, with an average growth rate of 1.5 percent. By 2015 it is expected to reach almost 200 million, which is the same year to achieve the millennium development goals (MDG’s). Important point to consider is that Pakistan is sixth most populous country of the world despite the continues falling growth rates; 36 percent of the total population lives in urban area rest of the population will be found in rural areas which is estimated to be 113 million people (CIA 2010). Total area of Pakistan is 796,095 square kilometers with population density of 220 persons per square kilometer, and with 2 percent of total world population living on less than 0.7 percent of total world land (The library of Congress, 2005).

Despite the considerable efforts different poverty alleviation programs, economic and widespread poverty still remain a problem in the country. The poor people in Pakistan are not only having low level of income, they are also lacking of access to basic services, such as proper education, clean drinking water, adequate sanitation, access to financial services, health facilities, employment opportunities and efficient market access (Government of Pakistan 2009: 43).

The figure above shows long term trend of poverty in Pakistan from 1987 to 2009. Poverty is measured by the consumption proportion of population below the poverty line. The Mid Term Development Framework of government has aim to reduce poverty to 21 percent during 2009 to 2010. The Millennium Development Goal of eliminating extreme poverty, hunger and halving the percentage of poor people earning less than 1.25 dollar a day between 1990 and 2015, its given in order to put the current situation in prespective. The given figure show that if Pakistan is to meet the set target, poverty have to be reduced to 13 percent by the year 2015. From estimates poverty in figure was around 40 percent in 2009, then the set target for the medium terms are unlikely to be achieved.

Ahmed and Donoghue (2010) says that the estimated poverty have climbed to 40 percent in 2009 which was 22 percent in 2006, that’s an increase of somewhat 80 percent. Other than that the country showed a poor performance in GDP growth rate i.e. only 1.2 percent in 2009. The poor performance is coupled with high inflation rate which was experienced during 2008 to 2009 and the country was also involved in internal and external conflicts. The recent floods in the country placed an additional burden on the economy and analysts say that it will take the country back by many years.

Microfinance in Pakistan

Microfinance origins in Pakistan can be traced back to early 1990’s, when two projects were running their programs: Aga Khan Rural Support Program (AKRSP) and Orangi Pilot Project. In 1999, the National Rural Support Program (NRSP) and AKRSP accounted 84 percent of total Microfinance services, and the only specialized Microfinance Institution was Kashf Foundation.

Today many other institutions provide Microfinance services in Pakistan. On the Microfinance Information exchange (online information service), 20 of these institutions are registered, 19 are members of Pakistan Microfinance Network (PMN). Most of these mentioned institutions are not specialized in Microfinance, but combine Microfinance with other developmental programs (health and education). The Microfinance provider in Pakistan can be classified into the following groups.

Microfinance Banks (MFBs): Specialized institutions that operate as microfinance banks

Rural Support Programs (RSP’s): Programs that run microfinance operations as a part of integrated rural development initiatives.

Nongovernmental Organizations/Microfinance Institutions (NGOMFI’s): Nongovernmental Organizations that run Microfinance operations as part of integrated development programs or that focus exclusively on Microfinance.

Commercial Financial Institutions: Commercial Institutions involved in Microfinance

Government owned institutions: Institutions involved in Microfinance that are owned by the government.

There are more or less 1400 branches of Microfinance providers in Pakistan about 1.5 million clients having gross loan portfolio of Rs. 15.1 billion ($250 million). Evidence shows that the Microfinance sector is dominated by one or two players and has heavy concentration of few institutions i.e. NRSP, Kashf Foundation and Khushhali Bank), these institutions serve 70 percent of the market. These microfinance institutions have the largest and fastest growth in the sector and have outpaced their competitors.

Most of the Microfinance providers are working in the Punjab and Sindh provinces and is due to their infrastructure and higher population density than the other provinces. In a survey it was revealed that about 60 percent of total active borrowers are from Punjab and 23 percent were from Sindh. Punjab and Sindh are densely populated provinces with 56 and 25 percent of total population of Pakistan, and this uneven penetration of Microfinance between provinces is due to the population distribution in the country (International Finance Corporation, 2008).

In Pakistan microfinance has yet to make breakthroughs to reach millions of people who are underserved and require a wide variety of financial products and services. Comparing the outreach of financial services in different countries, Pakistan has one of the lowest levels of financial penetrations in the world i.e. 56 percent of the adult population is totally excluded and 32 percent population is served informally. There is a considerable support from the government of Pakistan, State Bank of Pakistan and donors but still microfinance sector has only been able to outreach its product and services to a small fraction of potential market.

From 1999 to 2000 government of Pakistan took various initiatives were taken to construct the foundations for a national microfinance sector. The year 2007 was indicated as second phase in strategies and policies had been focused on accelerating growth through sustainable and scalable approaches. To promote sustainability and encourage a market driven formal system, SBP with stakeholder consultation have formulated a strategy called Expanding Microfinance Outreach (EMO) which was approved by the government of Pakistan in February 2007.

The strategy set a target of 3 million borrowers that was going to be achieved by the end of 2010 and 10 million by the end of 2015 provided a sustainable and commercial orientation to the sector. Key reforms were done under the Expanding Microfinance Outreach strategy and sector showed high annual of 43 percent in 2007 and 2008. However in the last quarter of 2008 growth rates declined since then growth has been sluggish, and stalling at 2 million microfinance borrowers.

Chapter 3

Conceptual Framework

Correlation

Out reach

Efficiency

Active Borrowers

Average loan balance per borrower

No of Deposits

Average Saving balance per saver

Operating cost ratio

Salaries and benefits

Cost per borrower

Return on Assets

Return on Equity

Operational self sufficiency

Capital/Asset Ratio

Theoretical Framework

The absolute number of poor people that have access to MFIs may be increased. Moreover, increased competition, technological change, and financial market policies, which focus on strengthening market forces and improving the stability of MFIs, may positively contribute to the efficiency of MFIs. This, in turn, may help generating more financial resources with which the poor can be helped. Under these circumstances, outreach and financial sustainability and efficiency seem to be compatible objectives.

Yet, focusing on financial sustainability and efficiency may also go at the cost of lending to the poor. As lending money to the poor especially the very poor and the rural poor can be very costly, the outreach and sustainability goal may be conflicting.

Methodology

The required data for the background and literature of this research is collected from secondary sources like internet, books, magazines, and digital libraries.

Furthermore to analyze the situation of Pakistan, four Microfinance Institutions are being taken i.e. Khushhali Bank, First Microfinance Bank, Kashf Foundation and National Rural Support Program (NRSP). These four Microfinance Institutions in which two are formal Microfinance Banks and the rest two are Semi formal Microfinance Institutions.

The collected data is analyzed using descriptive statistics and correlation measurement. The sampling technique which is used for the collection of data is "Judgmental sampling". As those microfinance institutions are taken of whom Financial Reports are available.

Following are the ratios through which data is being collected:

Efficiency

Operating cost ratio = Operating expense/ loan portfolio

Salaries and benefits = Average salary/ GNI per capita

Cost per borrower = Operating Expense / Average Number of Active Borrowers

Expense to Asset Ratio = (Financial Expense + Net Impairment Loss + Operating Expense) / Average Total Assets

Return on Assets = (Net Operating Income - Taxes) / Average Total Assets

Return on Equity = (Net Operating Income - Taxes) / Average Total Equity

Operational self sufficiency = Financial Revenue / (Financial Expense + Net Impairment Loss + Operating Expense)

Capital/Asset Ratio = Total Equity / Total Assets

Outreach

Active Borrowers = Number of borrowers with loans outstanding

Average loan balance per borrower = Gross Loan Portfolio / Number of Active Borrowers

Average outstanding balance = Gross Loan Portfolio / Number of Loans Outstanding

Deposits = Total value of all deposit accounts

Average Saving balance per saver = Deposits / Number of Depositors

GNI per capita ratio = Average Loan Balance per Borrower / GNI per Capita

Chapter 4

Interpretation of Measures of Central Tendency

The arithmetic average (termed the mean and abbreviated as) represents the most appropriate measure of central tendency for continuous-type data. It is obtained by adding all of the scores and dividing this sum by the number of scores.

Mean ( ) =

The mean can be denoted by (pronounced "X-bar") for samples and µ (pronounced "mu") when dealing with populations; ∑ denotes summation of a set of values; X represents the individual raw scores, and N equals the number of scores.

The median of a set of scores represents the middle value (50th percentile) when the scores are arranged as an array in order of increasing (or decreasing) magnitude. The median is often denoted by (pronounced "X-tilde"). The median often becomes a more appropriate (representative) measure of central tendency when the data are skewed that is, the majority of scores tend to accumulate toward either the high or low end of the distribution with a few extreme scores at the opposite end.

The result of my study shows that the Number of active borrowers, Number of depositors and Gross loan portfolio data collected is skewed and the majority of scores tend to accumulate towards the higher end of the distribution, so for these ratios I find median as an appropriate representative measure. Whereas the rest of the data collected via ratios is symmetrical and I find mean as an appropriate representative measure.

Interpretation of Correlation

The performance patterns of Pakistan Microfinance Institutions are studied by seeing the relationship of outreach to the poor and efficiency of these Microfinance Institutions, it’s done by applying the outreach and efficiency ratios (see Annexure) on four big Microfinance Institutions which represents 70 percent of Microfinance industry in Pakistan. The hypothesis that is tested is there any relationship between the outreach and efficiency in context of Pakistan microfinance institutions.

In Annexure 11 and 12, the relationship is first measured with scatter plot graph. In some cases the results is unable to be predicted and the scatter dots showed no associated such like in case of GNI per capita ratio and operational self sufficiency ratio showed no association and the dots were scattered. Whereas the most of the relationship of these sub variables shows that they have moderate positive correlation and the strength in the pattern shows how tightly clustered the points are around the underlying form. This means that as the values of one of the variables increase, the values of the second variable also increase. Likewise, as the value of one of the variables decreases, the value of the other variable also decreases.

To make it more appropriate I used the correlation method on the collected ratios to have more conclusive results. Appendix 4 contains the correlation that is applied on the ratios collected from the four microfinance institutions.

The relation between number of active borrower and operating cost ratio for three out of four MFI’s gives results in positive Pearson correlation i.e. when the number of active borrower’s increases there gets an increase in the operating cost ratio and vice versa.  The operating cost ratio is an indicator of how efficiently an organization is being managed, so with increase in number of active borrowers the operating expenses also increases, which show inefficiency of these microfinance institutions. The relationship between number of active borrowers and personnel expense ratio of the microfinance institutions results in positive Pearson correlation, that is when the number of active borrowers increase so there also occurs an increase in the personnel expense i.e. expense related to employees working in the organization. The increase in Personnel expense increases the overall operating cost which affects the efficiency of these microfinance institutions. Having said that, the relationship between number of active borrowers and cost per borrower show similar positive Pearson correlation result.

The relationship between the number of female borrowers and operating cost ratio between two microfinance institutions (Khushhali Bank and NRSP) results in positive Pearson correlation, and looking to the ratios we see that this positive correlation is due to the increase in the percentage of female borrowers and due to the increase in the percentage of female borrowers, we see a increase in the operating cost ratio. Whereas the relationship between First Microfinance Bank and Kashf Foundation shows negative Pearson correlation i.e. due to the increase in percentage of female borrowers, the operating cost ratio decreases and vice versa. Other than that percentage of female borrowers and microfinance institution operational self sufficiency have a positive correlation for all the microfinance institutions, when we see the ratio data we will get to know that the percentage of female borrowers have increased in past years, with operational self sufficiency of almost 100 percent for all microfinance institutions. That means increase in percentage of female borrower doesn’t decreases and has no effect on the institution operational self sufficiency.

The Pearson correlation between Gross loan portfolio and operating cost ratio for the microfinance institutions results positive, which is clear from the ratios that when there is an increase in the gross loan portfolio, there we see increase in the operating cost ratio. The increase in the gross loan portfolio increases the number of active borrowers and depositors, as when they are increased then we see an increase in the operating expenses. Having said that gross loan portfolio has also positive correlation with cost per borrower and it’s obvious that when there will be increase in number of active borrowers there will also be an increase in the cost per borrower.

Gross loan portfolio and expense to asset ratio has a positive Pearson correlation for all the four microfinance institutions which shows that increase in gross loan portfolio result in increase in expense to asset ratio that means that the increase in the number of active borrowers and depositors get an increase in the operating expenses and investment management fees of the portfolio over a specific period of microfinance institutions. The more the total expense ratio the more percentage of a fund’s assets will be required to cover these costs.

Average loan balance per borrower has a positive Pearson correlation with almost all of the efficiency ratio which means that the increase in it will result in increase in the efficiency ratios and vice versa. But I observed from the ratio data that the microfinance institutions average loan balance per borrower has increased each year which shows that lending to the well off people have increased and not targeting the poorest of the poor.

The GNI per capita ratio and operational self sufficiency shows a positive Pearson correlation. Average loan balance per borrower / GNI per capita is used to estimate the social outreach of microfinance institutions, the positive results shows that the increase in the social outreach, there gets an increase in operational self sufficiency.

Chapter 5

Conclusion and Recommendation

The objective of this project is to find out that is there any relationship between outreach and efficiency patterns of microfinance institutions of Pakistan both theoretically and empirically. On two aspects the assessment is been focused: the microfinance institutions capacity to reach out a large number of low income people with limited number of resources and secondly, the microfinance institutions capacity to manage these resources efficiently.

Worldwide there are many examples of institutions, that are able to attain both objectives i.e. efficiency and outreach. But in the whole Microfinance industry there is a clear lack of extensive and quality empirical studies. The data collected is representative of whole microfinance industry of Pakistan and has mainly focused on leading microfinance institutions. This study contributes to the research of outreach to poor while maintaining efficiency by performing an empirical analysis with a representative dataset from Pakistan. The analysis found that there is a negative relationship between outreach and efficiency. Therefore the thesis concludes that when variables of outreach increases there gets a decrease in the ratios of efficiency. Looking to that the microfinance institution will be inefficient. When the average loan balance per borrower / Gross National per capita ratio was calculated so it was found that each of the microfinance institution had a decreasing rate of it which showed that these microfinance institutions are more targeting socially excluded/impoverished clients. Targeting such clients carry cost which increases expenses and ultimately effect institution efficiency. But in actual the operational self sufficiency is 100 percent whereas for some microfinance institutions it was over 100 percent.

The market for microfinance is large and continues to grow. This rapid growth is illustrated from the microfinance institutions that were taken under consideration. Banking with the poor (Microfinance) can be a very profitable option. The key to this is to find the right factors combination to meet credit and savings needs of the poor. The one of the key solution to success in micro lending is to keep institution overheads very low.

Annexure 1

First Microfinance Bank

Outreach

Years

Number of active borrowers

Average loan balance per borrower

Number of depositors

Percent of female borrowers

Gross loan portfolio

GNI per capita ratio

2002

713

415

2,773

0.00%

296,068

81.42%

2003

3,558

312

9,919

0.00%

1,108,948

55.66%

2004

9,543

375

18,187

9.49%

3,573,861

58.52%

2005

16,931

358

18,589

14.49%

6,068,138

49.78%

2006

52,308

216

38,852

68.69%

11,307,140

27.36%

2007

101,394

196

79,827

42.23%

19,830,507

22.74%

2008

167,915

160

144,898

36.93%

26,811,464

16.99%

2009

150,102

165

189,878

34.82%

32,918,865

16.21%

2010

151,797

183

227,039

33.54%

27,732,246

17.22%

2011

119,204

225

240,394

33.46%

26,760,914

18.92%

Efficiency

Years

Operating cost ratio

Personnel expense ratio

Cost per borrower

Expense to Asset Ratio

Return on Assets

Return on Equity

Operational self sufficiency

Capital/Asset Ratio

2002

0

0

0

0

0

0

0

84.65%

2003

147.78%

72.03%

486

6.70%

0.03%

0.05%

103.99%

61.33%

2004

59.17%

27.50%

212

8.02%

0.06%

0.10%

105.23%

58.25%

2005

36.81%

17.04%

134

9.64%

-0.68%

-1.31%

93.92%

47.39%

2006

27.92%

14.21%

70

12.28%

0.97%

2.17%

113.42%

42.78%

2007

28.38%

13.78%

57

17.48%

-1.90%

-6.12%

90.43%

23.90%

2008

31.43%

15.03%

54

20.71%

-3.50%

-19.00%

83.20%

13.61%

2009

24.63%

12.63%

40

20.73%

0.46%

4.05%

102.74%

9.78%

2010

22.84%

11.19%

50

22.59%

-2.73%

-25.33%

88.83%

11.72%

2011

26.95%

13.81%

55

20.44%

-1.52%

-13.18%

93.52%

11.28%

Khushhali Bank

Outreach

Years

Number of active borrowers

Average loan balance per borrower

Number of depositors

Percent of female borrowers

Gross loan portfolio

GNI per capita ratio

2002

0

-

-

-

-

-

2003

91,532

130

-

37.64%

11,924,316

23.26%

2004

168,105

140

-

26.56%

23,542,172

21.88%

2005

227,172

142

-

33.33%

32,177,436

19.67%

2006

236,917

149

-

21.10%

35,351,635

18.89%

2007

283,965

152

-

15.43%

43,066,815

17.64%

2008

312,851

126

-

-

39,280,453

12.81%

2009

329,421

131

74,995

23.49%

43,011,904

12.84%

2010

325,523

134

205,962

25.67%

43,483,093

12.59%

2011

440,461

108

301,239

25.25%

47,513,089

9.09%

Efficiency

Years

Operating cost ratio

Personnel expense ratio

Cost per borrower

Expense to Asset Ratio

Return on Assets

Return on Equity

Operational self sufficiency

Capital/Asset Ratio

2002

-

-

-

-

-

-

-

2003

-

-

-

10.83%

-

-

62.07%

54.70%

2004

31.63%

14.54%

43

12.41%

-5.16%

-11.38%

52.98%

38.65%

2005

30.13%

14.62%

42

13.74%

-3.71%

-11.37%

72.18%

28.20%

2006

30.80%

16.68%

45

14.40%

-3.32%

-12.37%

76.89%

25.58%

2007

32.28%

16.90%

49

14.12%

-3.91%

-14.89%

79.70%

27.04%

2008

24.04%

12.08%

33

14.12%

-2.90%

-10.56%

79.48%

27.99%

2009

29.27%

16.63%

38

19.40%

-0.07%

-0.24%

100.12%

31.51%

2010

25.71%

15.13%

33

20.25%

1.10%

3.53%

106.41%

30.61%

2011

25.40%

14.93%

32

20.92%

0.59%

1.99%

99.34%

28.78%

NRSP

Outreach

Years

Number of active borrowers

Average loan balance per borrower

Number of depositors

Percent of female borrowers

Gross loan portfolio

GNI per capita ratio

2002

70,375

150

-

39.20%

10,540,722

29.37%

2003

88,401

162

-

23.03%

14,297,666

28.88%

2004

126,034

164

-

37.55%

20,653,666

25.61%

2005

190,846

174

-

-

33,170,935

24.14%

2006

292,456

183

-

42.74%

53,618,494

23.21%

2007

565,863

190

-

41.02%

107,662,816

22.12%

2008

399,969

141

-

52.02%

56,486,713

14.41%

2009

431,027

153

-

52.21%

65,862,911

15.03%

2010

317,381

134

-

74.80%

42,557,829

12.64%

2011

-

-

-

-

-

-

Efficiency

Years

Operating cost ratio

Personnel expense ratio

Cost per borrower

Expense to Asset Ratio

Return on Assets

Return on Equity

Operational self sufficiency

Capital/Asset Ratio

2002

-

-

24

-

-

-

79.52%

31.97%

2003

14.23%

7.43%

22

13.64%

0.37%

1.21%

102.74%

30.29%

2004

13.48%

6.98%

22

16.25%

-2.40%

-11.31%

85.26%

14.13%

2005

18.63%

11.17%

32

20.23%

-2.50%

-14.69%

87.64%

19.25%

2006

15.54%

9.50%

28

20.55%

0.24%

1.66%

101.18%

11.82%

2007

12.34%

7.30%

23

19.71%

4.02%

40.41%

120.38%

9.03%

2008

11.11%

6.90%

18

20.42%

0.56%

5.14%

102.73%

14.01%

2009

14.71%

9.16%

22

23.22%

2.31%

16.33%

109.95%

14.27%

2010

16.28%

8.67%

24

21.26%

5.62%

36.27%

126.45%

16.52%

2011

-

-

-

-

-

-

-

-

Kashf Foundation

Outreach

Years

Number of active borrowers

Average loan balance per borrower

Number of depositors

Percent of female borrowers

Gross loan portfolio

GNI per capita ratio

2002

29,655

76

26,791

100.00%

2,263,536

14.97%

2003

59,389

99

57,058

100.00%

5,849,770

17.59%

2004

67,552

119

54,042

100.00%

8,068,394

18.66%

2005

75,520

172

63,627

99.58%

12,956,833

23.83%

2006

133,690

188

106,952

98.36%

25,190,461

23.85%

2007

295,396

175

266,896

100.00%

51,603,641

20.31%

2008

313,512

141

-

100.79%

44,230,466

14.40%

2009

317,299

54

-

100.00%

6,890,838

5.43%

2010

224,140

110

-

-

17,226,943

10.35%

2011

286,443

109

80,067

100.00%

31,212,895

9.18%

Efficiency

Years

Operating cost ratio

Personnel expense ratio

Cost per borrower

Expense to Asset Ratio

Return on Assets

Return on Equity

Operational self sufficiency

Capital/Asset Ratio

2002

-

-

-

-

-

-

124.88%

83.23%

2003

10.82%

-

10

7.97%

2.72%

5.12%

134.13%

40.55%

2004

15.44%

9.56%

17

10.66%

9.51%

18.66%

189.20%

60.60%

2005

14.39%

9.23%

21

11.32%

8.55%

15.90%

175.49%

49.05%

2006

15.29%

10.51%

28

14.55%

7.76%

16.32%

153.31%

46.56%

2007

11.94%

7.75%

21

16.51%

10.31%

26.15%

162.45%

35.87%

2008

19.29%

8.72%

22

43.81%

-16.71%

-58.51%

77.13%

20.91%

2009

22.13%

-

19

23.78%

-5.17%

97.45%

78.24%

-2.42%

2010

22.24%

15.75%

20

26.84%

-4.26%

128.69%

84.12%

-4.28%

2011

19.96%

14.27%

22

27.13%

-0.11%

3.67%

99.59%

-1.91%

Annexure 2

First Microfinance Bank

Statistics

N

Mean

Median

Mode

Valid

Missing

No of active borrowers

10

9

77346.50

76851.00

713a

Average loan balance per borrower

10

9

260.5000

220.5000

160.00a

No of Depositors

10

9

97035.60

59339.50

2773a

Female Borrowers

10

9

27.3650

33.5000

.00

Gross Loan Portfolio

10

9

15640815

15568824

296068a

GNI per capital ratio

10

9

36.4820

25.0500

16.21a

Operating cost ratio

10

9

40.5910

28.1500

.00a

Personnel Expense ratio

10

9

19.7220

14.0100

.00a

Cost per borrower

10

9

115.8000

56.0000

.00a

Expense to Asset ratio

10

9

13.8590

14.8800

.00a

Return of Assets

10

9

-.8810

-.3400

-3.50a

Return on Equity

10

9

-5.8570

-.6550

-25.33a

Operational Self Sufficiency

10

9

87.5280

93.7200

.00a

Capital Asset ratio

10

9

36.4690

33.3400

9.78a

a. Multiple modes exist. The smallest value is shown

Khushhali Bank

Statistics

N

Mean

Median

Mode

Valid

Missing

No of active borrowers

10

9

241594.70

260441.00

0a

Average loan balance per borrower

10

9

121.2000

132.5000

.00a

No of Depositors

10

9

58219.60

.00

0

Female Borrowers

10

9

20.8470

24.3700

.00

Gross Loan Portfolio

10

9

31935091.30

37316044.00

0a

GNI per capital ratio

10

9

14.8670

15.2400

.00a

Operating cost ratio

10

9

22.9260

27.4900

.00

Personnel Expense ratio

10

9

12.1510

14.7750

.00

Cost per borrower

10

9

31.5000

35.5000

.00a

Expense to Asset ratio

10

9

14.0190

14.1200

14.12

Return of Assets

10

9

-1.7380

-1.4850

.00

Return on Equity

10

9

-5.5290

-5.4000

.00

Operational Self Sufficiency

10

9

72.9170

78.1850

.00a

Capital Asset ratio

10

9

29.3060

28.4900

.00a

a. Multiple modes exist. The smallest value is shown

NRSP

Statistics

N

Mean

Median

Mode

Valid

Missing

No of active borrowers

10

0

248235.20

241651.00

0a

Average loan balance per borrower

10

0

145.1000

157.5000

.00a

No of Depositors

10

0

.00

.00

0

Female Borrowers

10

0

36.2570

40.1100

.00

Gross Loan Portfolio

10

0

40485175.20

37864382.00

0a

GNI per capital ratio

10

0

19.5410

22.6650

.00a

Operating cost ratio

10

0

11.6320

13.8550

.00

Personnel Expense ratio

10

0

6.7110

7.3650

.00

Cost per borrower

10

0

21.5000

22.5000

22.00

Expense to Asset ratio

10

0

15.5280

19.9700

.00

Return of Assets

10

0

.8220

.3050

.00

Return on Equity

10

0

10.4400

3.4000

.00

Operational Self Sufficiency

10

0

91.5850

101.9550

.00a

Capital Asset ratio

10

0

16.1290

14.2000

.00a

a. Multiple modes exist. The smallest value is shown

Kashf Foundation

Statistics

N

Mean

Median

Mode

Valid

Missing

No of active borrowers

10

9

180259.60

178915.00

29655a

Average loan balance per borrower

10

9

124.3000

114.5000

54.00a

No of Depositors

10

9

65543.30

55550.00

0

Female Borrowers

10

9

89.8730

100.0000

100.00

Gross Loan Portfolio

10

9

20549377.70

15091888.00

2263536a

GNI per capital ratio

10

9

15.8570

16.2800

5.43a

Operating cost ratio

10

9

15.1500

15.3650

.00a

Personnel Expense ratio

10

9

7.5790

8.9750

.00

Cost per borrower

10

9

18.0000

20.5000

21.00a

Expense to Asset ratio

10

9

18.2570

15.5300

.00a

Return of Assets

10

9

1.2600

1.3600

-16.71a

Return on Equity

10

9

25.3450

16.1100

-58.51a

Operational Self Sufficiency

10

9

127.8540

129.5050

77.13a

Capital Asset ratio

10

9

35.8850

38.2100

-4.28a

a. Multiple modes exist. The smallest value is shown

Correlations

No of active borrowers

Average loan balance per borrower

No of Depositors

Female Borrowers

Gross Loan Portfolio

GNI per capital ratio

Operating cost ratio

Personnel Expense ratio

Cost per borrower

Expense to Asset ratio

Return of Assets

Return on Equity

Operational Self sufficiency

Capital Asset ratio

No of active borrowers

Pearson Correlation

1

-.910**

.889**

.576

.977**

-.898**

-.393

-.380

-.514

.945**

-.651*

-.656*

.224

-.936**

Sig. (2-tailed)

.000

.001

.081

.000

.000

.262

.279

.128

.000

.042

.040

.534

.000

N

10

10

10

10

10

10

10

10

10

10

10

10

10

10

Average loan balance per borrower

Pearson Correlation

-.910**

1

-.748*

-.767**

-.894**

.963**

.193

.174

.331

-.917**

.471

.484

-.462

.908**

Sig. (2-tailed)

.000

.013

.010

.000

.000

.594

.630

.350

.000

.169

.156

.179

.000

N

10

10

10

10

10

10

10

10

10

10

10

10

10

10

No of Depositors

Pearson Correlation

.889**

-.748*

1

.413

.926**

-.800**

-.346

-.331

-.443

.890**

-.538

-.664*

.205

-.885**

Sig. (2-tailed)

.001

.013

.236

.000

.005

.327

.350

.200

.001

.109

.036

.571

.001

N

10

10

10

10

10

10

10

10

10

10

10

10

10

10

Female Borrowers

Pearson Correlation

.576

-.767**

.413

1

.587

-.778**

-.413

-.398

-.495

.634*

-.132

-.196

.449

-.627

Sig. (2-tailed)

.081

.010

.236

.075

.008

.236

.255

.146

.049

.716

.588

.193

.052

N

10

10

10

10

10

10

10

10

10

10

10

10

10

10

Gross Loan Portfolio

Pearson Correlation

.977**

-.894**

.926**

.587

1

-.913**

-.410

-.395

-.526

.957**

-.530

-.551

.284

-.960**

Sig. (2-tailed)

.000

.000

.000

.075

.000

.240

.259

.119

.000

.115

.099

.427

.000

N

10

10

10

10

10

10

10

10

10

10

10

10

10

10

GNI per capital ratio

Pearson Correlation

-.898**

.963**

-.800**

-.778**

-.913**

1

.234

.219

.356

-.969**

.485

.511

-.567

.969**

Sig. (2-tailed)

.000

.000

.005

.008

.000

.516

.544

.312

.000

.155

.131

.088

.000

N

10

10

10

10

10

10

10

10

10

10

10

10

10

10

Operating cost ratio

Pearson Correlation

-.393

.193

-.346

-.413

-.410

.234

1

.999**

.988**

-.267

.209

.221

.425

.269

Sig. (2-tailed)

.262

.594

.327

.236

.240

.516

.000

.000

.457

.562

.540

.221

.453

N

10

10

10

10

10

10

10

10

10

10

10

10

10

10

Personnel Expense ratio

Pearson Correlation

-.380

.174

-.331

-.398

-.395

.219

.999**

1

.984**

-.255

.213

.220

.428

.257

Sig. (2-tailed)

.279

.630

.350

.255

.259

.544

.000

.000

.477

.554

.541

.217

.474

N

10

10

10

10

10

10

10

10

10

10

10

10

10

10

Cost per borrower

Pearson Correlation

-.514

.331

-.443

-.495

-.526

.356

.988**

.984**

1

-.382

.278

.281

.373

.384

Sig. (2-tailed)

.128

.350

.200

.146

.119

.312

.000

.000

.276

.437

.431

.288

.273

N

10

10

10

10

10

10

10

10

10

10

10

10

10

10

Expense to Asset ratio

Pearson Correlation

.945**

-.917**

.890**

.634*

.957**

-.969**

-.267

-.255

-.382

1

-.601

-.630

.481

-.995**

Sig. (2-tailed)

.000

.000

.001

.049

.000

.000

.457

.477

.276

.066

.051

.159

.000

N

10

10

10

10

10

10

10

10

10

10

10

10

10

10

Return of Assets

Pearson Correlation

-.651*

.471

-.538

-.132

-.530

.485

.209

.213

.278

-.601

1

.914**

.062

.570

Sig. (2-tailed)

.042

.169

.109

.716

.115

.155

.562

.554

.437

.066

.000

.865

.086

N

10

10

10

10

10

10

10

10

10

10

10

10

10

10

Return on Equity

Pearson Correlation

-.656*

.484

-.664*

-.196

-.551

.511

.221

.220

.281

-.630

.914**

1

.022

.579

Sig. (2-tailed)

.040

.156

.036

.588

.099

.131

.540

.541

.431

.051

.000

.951

.079

N

10

10

10

10

10

10

10

10

10

10

10

10

10

10

Operational Self sufficiency

Pearson Correlation

.224

-.462

.205

.449

.284

-.567

.425

.428

.373

.481

.062

.022

1

-.490

Sig. (2-tailed)

.534

.179

.571

.193

.427

.088

.221

.217

.288

.159

.865

.951

.150

N

10

10

10

10

10

10

10

10

10

10

10

10

10

10

Capital Asset ratio

Pearson Correlation

-.936**

.908**

-.885**

-.627

-.960**

.969**

.269

.257

.384

-.995**

.570

.579

-.490

1

Sig. (2-tailed)

.000

.000

.001

.052

.000

.000

.453

.474

.273

.000

.086

.079

.150

N

10

10

10

10

10

10

10

10

10

10

10

10

10

10

**. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

Correlations

No of active borrowers

Average loan balance per borrower

No of Depositors

Female Borrowers

Gross Loan Portfolio

GNI per capital ratio

Operating cost ratio

Personnel Expense ratio

Cost per borrower

Expense to Asset ratio

Return of Assets

Return on Equity

Operational Self Sufficiency

Capital Asset ratio

No of active borrowers

Pearson Correlation

1

.530

.673*

.118

.967**

.023

.697*

.783**

.640*

.919**

.107

.042

.901**

.170

Sig. (2-tailed)

.115

.033

.745

.000

.950

.025

.007

.046

.000

.769

.908

.000

.639

N

10

10

10

10

10

10

10

10

10

10

10

10

10

10

Average loan balance per borrower

Pearson Correlation

.530

1

-.014

.560

.654*

.837**

.700*

.679*

.711*

.704*

-.435

-.453

.737*

.708*

Sig. (2-tailed)

.115

.969

.092

.040

.003

.024

.031

.021

.023

.209

.189

.015

.022

N

10

10

10

10

10

10

10

10

10

10

10

10

10

10

No of Depositors

Pearson Correlation

.673*

-.014

1

.206

.523

-.350

.149

.280

.056

.645*

.639*

.655*

.566

.021

Sig. (2-tailed)

.033

.969

.567

.121

.322

.681

.432

.879

.044

.047

.040

.088

.954

N

10

10

10

10

10

10

10

10

10

10

10

10

10

10

Female Borrowers

Pearson Correlation

.118

.560

.206

1

.128

.685*

.144

.147

.115

.439

.050

.144

.401

.742*

Sig. (2-tailed)

.745

.092

.567

.724

.029

.691

.685

.751

.204

.892

.691

.251

.014

N

10

10

10

10

10

10

10

10

10

10

10

10

10

10

Gross Loan Portfolio

Pearson Correlation

.967**

.654*

.523

.128

1

.144

.806**

.879**

.771**

.912**

-.040

-.124

.918**

.177

Sig. (2-tailed)

.000

.040

.121

.724

.692

.005

.001

.009

.000

.913

.733

.000

.625

N

10

10

10

10

10

10

10

10

10

10

10

10

10

10

GNI per capital ratio

Pearson Correlation

.023

.837**

-.350

.685*

.144

1

.343

.261

.379

.281

-.549

-.501

.305

.818**

Sig. (2-tailed)

.950

.003

.322

.029

.692

.332

.467

.280

.431

.100

.141

.391

.004

N

10

10

10

10

10

10

10

10

10

10

10

10

10

10

Operating cost ratio

Pearson Correlation

.697*

.700*

.149

.144

.806**

.343

1

.983**

.991**

.673*

-.527

-.524

.634*

.085

Sig. (2-tailed)

.025

.024

.681

.691

.005

.332

.000

.000

.033

.118

.120

.049

.815

N

10

10

10

10

10

10

10

10

10

10

10

10

10

10

Personnel Expense ratio

Pearson Correlation

.783**

.679*

.280

.147

.879**

.261

.983**

1

.965**

.758*

-.375

-.396

.724*

.066

Sig. (2-tailed)

.007

.031

.432

.685

.001

.467

.000

.000

.011

.286

.257

.018

.857

N

10

10

10

10

10

10

10

10

10

10

10

10

10

10

Cost per borrower

Pearson Correlation

.640*

.711*

.056

.115

.771**

.379

.991**

.965**

1

.607

-.598

-.614

.583

.068

Sig. (2-tailed)

.046

.021

.879

.751

.009

.280

.000

.000

.063

.068

.059

.077

.853

N

10

10

10

10

10

10

10

10

10

10

10

10

10

10

Expense to Asset ratio

Pearson Correlation

.919**

.704*

.645*

.439

.912**

.281

.673*

.758*

.607

1

.146

.126

.981**

.454

Sig. (2-tailed)

.000

.023

.044

.204

.000

.431

.033

.011

.063

.687

.728

.000

.187

N

10

10

10

10

10

10

10

10

10

10

10

10

10

10

Return of Assets

Pearson Correlation

.107

-.435

.639*

.050

-.040

-.549

-.527

-.375

-.598

.146

1

.961**

.155

-.066

Sig. (2-tailed)

.769

.209

.047

.892

.913

.100

.118

.286

.068

.687

.000

.669

.856

N

10

10

10

10

10

10

10

10

10

10

10

10

10

10

Return on Equity

Pearson Correlation

.042

-.453

.655*

.144

-.124

-.501

-.524

-.396

-.614

.126

.961**

1

.092

.003

Sig. (2-tailed)

.908

.189

.040

.691

.733

.141

.120

.257

.059

.728

.000

.801

.992

N

10

10

10

10

10

10

10

10

10

10

10

10

10

10

Operational Self Sufficiency

Pearson Correlation

.901**

.737*

.566

.401

.918**

.305

.634*

.724*

.583

.981**

.155

.092

1

.465

Sig. (2-tailed)

.000

.015

.088

.251

.000

.391

.049

.018

.077

.000

.669

.801

.175

N

10

10

10

10

10

10

10

10

10

10

10

10

10

10

Capital Asset ratio

Pearson Correlation

.170

.708*

.021

.742*

.177

.818**

.085

.066

.068

.454

-.066

.003

.465

1

Sig. (2-tailed)

.639

.022

.954

.014

.625

.004

.815

.857

.853

.187

.856

.992

.175

N

10

10

10

10

10

10

10

10

10

10

10

10

10

10

*. Correlation is significant at the 0.05 level (2-tailed).

**. Correlation is significant at the 0.01 level (2-tailed).

Correlations

No of active borrowers

Average loan balance per borrower

No of Depositors

Female Borrowers

Gross Loan Portfolio

GNI per capital ratio

Operating cost ratio

Personnel Expense ratio

Cost per borrower

Expense to Asset ratio

Return of Assets

Return on Equity

Operational Self Sufficiency

Capital Asset ratio

No of active borrowers

Pearson Correlation

1

.501