Department of Management Sciences

WORKING PAPER SERIES

Islamic Microfinance and Household Welfare Nexus:  Empirical Investigation from Pakistan

Working Paper                Hafiz Zahid Mahmood

2016-04                             Kausar Abbas

Mehreen Fatima

Department of Management Sciences

COMSATS Institute of Information Technology, Lahore, Pakistan

DMS working papers are circulated for discussion and comments only. The views expressed in working paper are by authors only. All rights reserved by the Department of Management Sciences. 

Islamic Microfinance and Household Welfare Nexus: Empirical Investigation from Pakistan

Hafiz Zahid Mahmood

COMSATS Institute of Information Technology,

Lahore, Pakistan

drhafizzahid@ciitlahore.edu.pk

Kausar Abbas

Center of Islamic Finance (CIF)

COMSATS Institute of Information Technology,

Lahore, Pakistan

kausarabbas@ciitlahore.edu.pk kausarsial@yahoo.com

Mehreen Fatima

COMSATS Institute of Information Technology,  Lahore, Pakistan

mehrinf@gmail.com

Abstract

Many approaches and tools have been utilized throughout the globe by public and private sector organizations to curtail the deprivations and enhance welfare of the poor. Islamic Microfinance is one of them and is rapidly getting popular in Muslims as well non Muslims majority population countries. This study was conducted to gauge the impact of Islamic microfinance on the household welfare of the target clients by observing its impact on health, education, income, expenditures and assets of the poor who took loan from IMF institutions. Study is based on primary data which was collected through structured questionnaires. The assessment was made rendering pre and post project approach by employing group t-test and Regression as a statistical tools. Respondents were selected from three microfinance institutions, namely Akhuwat Foundation, Farz Foundation and NAYMET. Results of the study delineate that IMF has amply spread welfare impact on target borrowers. Therefore, it is recommended that practitioners and policy makers must keep IMF on its top agenda to enhance living standards of the poor in developing countries.

Keywords: Poverty, Microfinance, Impact assessment, Islamic microfinance, Islamic finance, social welfare, Assets,

1 Introduction

The concept of poverty ranges from low income and consumption level to limited or no access to education, housing and health services. It also includes social aspects like subjection, lack of security, defenselessness, gender, seclusion and social barring and environmental disparities. Visconti (2012) stated in his study that poverty is the aftermath of various causes including insufficient access to money, monopolies, unequal distribution of wealth, strong and well-built intermediaries, and absence of democracy. Developing countries are the hub of poor sheltering 48 percent of global deprived living below two dollars a day (WB, 2010). The number of hungry people has reached 963 million with significant increment of 142 million in the world poor since 1990, which makes 15% of the world population (Govt. of Pakistan, 2010; OECD and FAO, 2009). Muslim population stretching from Senegal to Philippines belonging to various regions is also victim of this phenomenon with very few exceptions of Southeast Asia and Middle East countries. Bangladesh and Pakistan are custodians of 122 million poor followed by India with 100 million people living below poverty line (Obaidullah, 2008).

Microfinance took roots in Pakistan in 1970s with the establishment of Agriculture development

Bank of Pakistan (ADBP) to serve rural farmers by providing them with subsidized credit (Rauf & Mahmood, 2009). Basic objective behind introducing microfinance was the welfare of the poor. With time, Microfinance Institutions (MFIs) and NGOs took roots in the field of microfinance to serve the poor. Khushali Bank pioneered in the field of microfinance in Pakistan as it was the first microfinance bank founded in year 2000. Microfinance Ordinance and Prudential Regulations were introduced later in 2001 in Pakistan. Pakistan is among those few countries around the world having legal and regulatory framework for microfinance banks (Allen & Overy LLP, 2009). According to Pakistan Microfinance Network, microfinance activity is performed by 11 Microfinance Banks (MFBs), 11 Microfinance Institutions (MFIs), 5 Rural Support Programs (RSPs) in Pakistan.

2  Literature review

In the world of financing, Islamic finance has flourished enormously with a significant trend (Gustina and Ihsan, 2010). Iqbal and Molyneux (2005) stated that Islamic banking has been found as the rapid growing industry compared to other industries. Islamic microfinance is embracing predominantly the welfarist approach and has only half percent of total microfinance global outreach (ADB, 2009). Moreover, 59 percent women are the beneficiary of IMF. Islamic microfinance is the convergence of Islamic finance and microfinance. Both of them are emerging industries in Pakistan. It has immense potential to amalgamate the Islamic principle of concern for the poor and miserable with the mission of microfinance to reach the poor and give them financial access (Karim et al., 2008).

As outreach is considered as one of the goals of microfinance, therefore the two perspectives of microfinance, i.e. welfarist and institutionalist perspectives (Robinson, 2001; Fisher, 2002; Woller et al, 1999; Morduch, 2000), are based on achieving outreach. Providing financial services to the poor along with the financial sustainability of the institution is the key purpose of Institutionalist approach. This approach is market-oriented which excludes the extreme poor and any social impact is taken only as the by-product of the whole process. This approach believes in the notion that poor are not only creditworthy but also profitable. This approach focuses on breadth of outreach (numbers of clients) and financial sustainability where savings mobilization is an important feature. This approach advocates commercialization of the institutions in order to achieve larger outreach for long terms (Robinson, 2001). Institution is the center of attention and success is determined by the financial profits and sustainability.

Instead of ‘end’, i.e. outreach, institutional approach is centered on ‘means’, i.e. sustainability, to reach the target and this could also hamper the accomplishment of target itself by being more focused on sustainability then the poor (Robinson, 2001). Institutionalist approach is criticized for overlooking the goal of reaching the poor thus marginalizing the poorest of poor and emphasizing largely on sustainability (Fisher, 2002). It is argued that social and profit-making objectives need to be balanced which can be realized when client and market needs are in equilibrium (Wrenn, 2005).

Poverty mitigation and empowering the poor are recognized as the chief aims of Welfarist approach which makes the welfare of the poor its foundation. This approach focuses on the depth of outreach (levels of poverty reached) and its resulting social impact for poor. Credit is considered as a means to an end where ‘end’, i.e. outreach, is more important than ‘means’, i.e. sustainability. Their interest is not in banking rather in alleviating poverty among the clients, including the poorest of the poor, by using financial services. They insist that emphasizing upon the depth of outreach is the key to building a sustainable institution. This approach does not take savings mobilization as compulsory feature and is not much excited about money-making and sustainability.

This approach has been criticized by many researchers as microfinance institutions might run into loss without sustainability which could lead to less number of poor to be served and even shutting down. In Robinson’s (2001) point of view “…even successful institutions following the poverty lending approach, in aggregate; can meet only a small portion of the demand for microfinance”. Taking this into consideration it could be safely said that sustainability is very important as, in Woller’s (1999) words, “donors would not offer long-term funding”. Savings have been overlooked (Vogel, 1984) which are even more important for poor. Robinson (2001) also stated that welfarist approach offers only credit to its clients for poverty alleviation where savings and insurance are also significant for achieving this goal of poverty eradication.

Although many microfinance institutions around the world are practicing both approaches nonetheless there exists a huge rift between them. Both approaches are need in today’s scenario as welfare is the key goal which cannot be achieved without sustainability. Therefore a possible combination is required where both work together. It is argued in literature that such services are required to be designed that retain high standards of profitability and embarking upon new standards in social impact of this activity (Wrenn, 2005).Institutionalist approach argues that the fundamental aim of microfinance is to build a financial system that is sustainable and that can provide financial services to greater number of poor. The impact, this system brings in the life of borrower, is not their concern. Impact studies are being emphasized upon by welfarist approach to measure the changes in the living condition of borrower after lending. This paper supports welfarist approach and is an impact study of Islamic microfinance institutions working in Pakistan.

3  Data and Methods

Current study is an effort to evaluate and observe the impacts of various Islamic modes of financing on household welfare of the poor of Pakistan. It is noteworthy here that to evaluate the performance of the target institutions pre and post project evaluation approach is implemented. Respondent having borrowed from the organizations with minimum period of at least three or more than three years as per availability from various target lending institutions. Three Islamic microfinance institutions were selected namely, Akhuwat Foundation, NAYMET (Naziran Yousaf Memorial Trust), and Farz foundation.475clients of these three institutions are population for this study. 320 clients are from Akhuwat Foundation while 85 are from Farz

Foundation and 70 from NAYEMT. At least 33% of respondents are taken from each institution. 112 respondents from Akhuwat Foundation, 31 from Farz Foundation and 25 from NAYMET were selected.

Clients of these institutions are selected with purposive non-probability sampling technique. It is used when respondents are selected through deliberate judgments with respect to particular group or area (Kerlinger, 1986). In purposive sampling, sample is confined to certain type of people who either have the required information or they match the criteria laid down by the researcher (Sekaran & Bougie, 2010). Purposive sampling technique is used in our study as Akhuwat

Foundation, one of the three institutions of our population, is quite big in number of its clients while the other 2 are quite small. In this situation, using some other sampling technique will not represent small institutions since Akhuwat Foundation will dominate and monopolize the results. Secondly, we selected respondents from certain areas served by the Islamic microfinance institution considering the ease of access. Still another reason for using this technique is that we have selected only those clients of the organization who have taken loan 3 or more than 3 years back. Data is collected through face to face interviews.

In t-statistics we have used pre and post project approach for evaluation that suggested the situation before and after the project. We want to find out the difference in household welfare before and after taking loan. To see this difference in pre and post scenario, we have taken six variables namely monthly income, monthly expenditure, monthly food expenditure, assets, schooling and health. T-test is be applied to observe the difference between current income and consumption of last 3 or more then 3 years of the clients of the organizations under study. We have applied multiple linear regression to check the impact of our predictors on income level of respondents. Logit Model has been used to gauge the probability of the respondents being poor having borrowed from the Islamic microfinance institutions.

4  Results and discussion

The results shows that 96 out of 168 clients are with no education which means more than half of the clients are illiterate (Table 1). While a big number of clients got education only till primary with very few got chance to educate themselves above this level. Results for Akhuwat Foundation show that almost half of clients are illiterate. In case of Farz Foundation 25 out of 31 are illiterate. NAYMET also shows a bigger percentage of clients as illiterate.

There were more literate clients of Akhuwat Foundation as compare to Farz Foundation and NAYMET. Targeting only females can be a big reason. In Pakistan, male child got more chance of getting education especially in lower class. As Akhuwat Foundation is targeting the whole family therefore they have more male clients as compare to other two institutions. More male clients lead to having more literate clients. The bar chart below shows the percentage of illiterate, primary, middle and matriculate respondents of our sample from the three Islamic microfinance institutions.

Table 1: Literacy rate of respondents

Literacy among respondents Akhuwat Foundation Farz Foundation NAYMET Total
Literate Primary 38 3 6 47
Middle 17 0 1 18
Matriculation & above 3 3 1 7

Total literate 58 , 6 , 8 , 72
Illiterate 54 , 25 , 17 , 96

Akhuwat Foundation disbursed maximum mean amount of loan (Rs. 21,375) amongst its clients followed by NAYMAT (Rs. 16,120), and Farz Foundation (Rs. 11,823). However, overall average funds disbursement was found Rs. 18830 to the clients of all of the institutions under study. The three institutions have depicted quite different standard deviations from the mean loan amount which are shown in figure 1. This might be due to the fact that all three institutions offer different amounts of loan so deviation of loan amount from the mean also varies.

Source: From Survey Data

Figure-1: Descriptive Statistics of Mean Loan Amount

We have applied paired sample t-test (Table 2) and used pre and post project approach for evaluation that suggested the situation before and after the project. To see this difference in pre and post scenario, we have taken six variables namely monthly income, monthly expenditure, monthly food expenditure, assets, schooling and health.

Mean difference of monthly income is given of the two variables i.e. -14316.161 (Table 2). There is significant difference in total monthly income before and after taking loan as p<0.01. It showed that taking loan was in fact effective. Negative t value also shows that mean for income after taking loan is higher. Mean for total income before taking loan is 11271.02 while mean for total income after taking loan is 25587.18. Both means are statistically significantly different from each other. Therefore paired sample t-test shows that respondents were getting more income after taking loan.

Mean for total expenditure before taking loan is 14091.28 while mean for total expenditure after taking loan is 25062.13. Both means are statistically significantly different from each other with p value less than .01. This is evident from the mean difference as well. Negative t value also shows that mean for income after taking loan is higher. It showed that taking loan was in fact effective. Therefore paired sample t-test suggests that respondents have more money to spend after taking loan. These results are in line with work of Coleman (2002) who also reported positive impact of microfinance activities on the income of borrowers. The results are exhibiting a rise in the welfare of the Islamic microfinance clients after taking loan.

Table 2: Impact of Islamic microfinance on household’s welfare (t-test Results)
Variables Mean before taking loan Mean after taking loan Mean

Difference

Sig. (2tailed)
Total Monthly income 11271 25587 -14316 .000
  Monthly expenditure 14091 25062 -10971 .000
  Monthly food expenditure 10640 17132 -6492 .000
  Assets 1.0298 3.0595 -2.02976 .000
  Education 62 389 -327 .000
  Health 527 1506 -980 .000
Akhuwat Monthly income 9749 24002 -14252 .000
Monthly expenditure 11715 23370 -11655 .000
Monthly food expenditure 8509 15205 -6696 .000
Assets .8571 2.8839 -2.02679 .000
Education 51 321 -270 .000
Health 474 1551 -1078 .000
Farz

Foundation

Monthly income

Monthly expenditure

14568

20204

30540

29768

-15972

-9564

.000

.000

Monthly food expenditure 15983 22071 -6089 .000
Assets 1.0968 3.5161 -2.41935 .000
Education 103 526 -423 .000
Health 690 1529 -839 .000

NAYMET      Monthly income                         14000                 26550               -12550            .000

Monthly expenditure 17285 26881 -9596 .000
Monthly food expenditure 13563 19642 -6079 .000
Assets 1.7200 3.2800 -1.56000 .000
Education 60 524 -464 .000
Health 562 1276 -714 .000

 

P value (p<.01) for food expenditure is highly significant which display significant difference in total food expenditure before and after taking loan. Therefore, taking loan was in fact effective. Negative t value also shows that mean for expenditure on food after taking loan is higher overall. Mean for food expenditure before taking loan is 10640.22 while mean for to food expenditure after taking loan is 17131.93. Both means are statistically significant at .01 level. Therefore, paired sample t-test shows that respondents have more money to spend on food after taking loan and it also helps us to understand the welfare situation of the respondents under study. Mean for assets possession before taking loan is 1.0298 while mean for assets possession after taking loan is 3.0595. This difference is statistically significant (p<.01) therefore, paired sample t-test shows that respondents have more assets after taking loan.

Mean differences of monthly income for all three institutions shows enough difference. Both means of variables are statistically significantly different from each other. Therefore paired sample t-test shows that respondents are better off after taking loan in all three organizations. All the t-test results for three institutions are significant at 0.01 which means there is significant difference in monthly income, monthly expenditure, and expenditure for food and assets possession before and after taking loan. Negative t values for all variable show that respondents are better off after taking loan for all these variables.  Highest mean difference for monthly income and assets is of the clients of Farz Foundation. While highest mean difference for monthly expenditure and food expenditure is of the clients of Akhuwat Foundation.

4.1 Assets:

Coleman (2002) reported in his study that microfinance has affected considerably and positively the household assets. Filmer and Pritchett (2001) also used assets in their study as a variable to measure income or wealth. Moreover, in the report of UNCDF (2004), assets are considered as an indicator to judge the impact of micofinance. Therefore assets are also used in this study as an indicator of household welfare to measure the impact of islamic microfiannce. Table 2 shows the results of assets possession. The possession of assets shows that there is quite a huge difference in assets possession for some assets while for some others there is less or no difference at all (Table 3).

The biggest positive difference is found in Bicycle, washing machine, sewing machine and gas cylinder. While less change is found in the possession of motor cycle, TV and refrigerator and very less or no change is found in the possession of assets like car, PTCL landline, microwave, toaster, sandwich maker and air conditioner. This table demonstrates that there is considerable change in the possession of assets like Bicycle, washing Machine, Sewing Machine, TV and Gas cylinder. It means taking loan affects the lives of clients in a positive manner. This shows that poverty also is associated with lack of asset ownership. As poor people get better-off after taking loan, their asset possession has shown significant change.

Table 3: Overall Descriptive statistics of Assets
Asset/ Institutions %age of possession before taking loan %age of possession after taking loan
Akhuwat Foundation Farz Foundation NAYMET Akhuwat Foundation Farz Foundation NAYMET
Car 0 0 0 2 0 0
Motor Cycle 0 0 0 10 26 20
Bicycle 2 10 20 38 52 36
Washing Machine 13 13 20 43 58 40
Sewing Machine 2 10 24 52 45 72
TV 11 13 16 19 48 36
PTCL 0 0 0 0 0 0
Refrigerator 0 0 8 10 19 16
Gas Cylinder 4 13 24 58 42 48
Microwave 1 0 0 0 3 0
Toaster 0 0 0 0 0 0
Sandwich Maker 0 0 0 3 7 0
Air Conditioner 0 0 0 1 3 0

4.2  Regression analysis

Regression analysis is applied in order to find out the impact of different predictors on the two outcome variables i.e. total income and poverty. For this purpose Multiple Linear Regression and Logistic regression are used. In order to test the impact of independent variables on dependent variable which is total income of respondents, Model has been analyzed using multiple linear regression. We have run backward step wise regression and the best model is selected which is shown in Table 4.

The value of R2in regression informs us that the predictors can create variation in the outcome variable by that percentage. For our model, the value of R2 is .262 which means our predictors can create variation in dependent variable, which is total income, by 26.2%. This low percentage is because income is a multidimensional phenomenon and we cannot say that few variables affect it completely. Rahman (2010) also stated in his study that income depends on many socioeconomic factors. For this model, F is 11.521 which is significant at less than 1% with p<0.01.

Table 4: Multi linear Regression
Model B Sig. Collinearity Statistics
Tolerance VIF
(Constant) 13078.81 .000*
Age 74.79 .107 .795 1.258
Gender  -3207.6 .000* .858 1.166
Family members 215.01 .371 .940 1.064
Loan amount -.15 .001* .871 1.148
Management

attributes/level

153.40 .014** .804 1.243
F=11.521 R2=.262
Dependent variable: Total income

* Statistically significant at less than 1% level based on two-tailed tests.

** Statistically significant at less than 5% level based on two-tailed tests.

*** Statistically significant at less than 10% level based on two-tailed tests.

Here we have five independent variables namely age of household head, gender of household head, family members of respondent, loan amount that respondent borrowed from the Islamic microfinance institution and the management level. Due to specification errors, we dropped some independent variables. We have run step wise regression in SPSS to analyze our model.

In Table 4, values of B tell about the relationship between dependent variable (total income) and each predictor. Positive values show positive relationship which means as the independent variables increases so does the dependent variable. Negative values of B shows that the relationship is negative or inverse that mean as the value of independent variable increases, dependent variable will decrease. Three out of five predictors (age, family members, and management level) show positive B values which means they have positive relationship with the outcome variable. While two predictors (gender and loan amount) displayed negative B values which tell that they have negative relationship with the dependent variable i.e. total income. The value of constant is 13078.811 which show that when there is no change in the independent variables, the total income is this much.

Here age is insignificant in the above regression results but as it is 89% significance which is very close to 90% so it can be considered almost significant. B value for age is positive (74.785) which tells that age has positive relationship with the dependent variable total income. If predictor is increased by one unit, in this case it is number of years of age of borrower; our model predicts that the total income will increase by Rs. 74.785. It means although age has quite less effect on the income but it certainly has some. Increase in the age of the borrower increase the total income earned. This is because the young people normally do not take things seriously but as they get older, things change for them. They got married and have children and have to support their parents who were until then supporting them. This situation makes them increase their income. That is why as the age of the borrower increase their level of income increases as well. Apart from this, in a developing country like Pakistan, people usually work till late age especially if he or she belongs to poor family.

Gender is significant at less than 1% with p<0.01. Negative B value tells that gender has a negative relationship with total income. We took 1 for male and 0 for female in our data.  Negative B values means if the borrower is male; the income earned will be 3207.598 rupees less as compare to if the borrower is a female. It explains that female borrowers perform in earning better after taking loan as compare to male borrowers. This means women are more likely to spend loan in an effective way and less likely to default when given loan. These results match with the results of Pitt et al. (2003), who reported in their study that borrowing by females shown positive impact on the health of children while borrowing by males shown either none or negative impact on their children health.

The predictor, Family members, has been found insignificant for dependent variable with p value (.371>0.05). This is probably because in poor families, more than one family member usually works to earn living and so increase in family members usually does not affect the total income in negative manner.

Loan amount is highly significant with p<0.01 and t statistics -3.333 but its negative B value (.151) shows that it has an inverse relationship with the total income. Now, loan amount being significant is quite justified that it has impact on the dependent variable but its negative B value is strange enough. It tells that as the loan amount increases, the total income decreases. This can be justified by law of diminishing returns. The output increases till a certain threshold level with the increase in input. Output starts to decrease after the threshold level with increase in input. If the borrower gets large amount in loan and found it more then he required in business, chances arise that he will spend some part of it for some other purpose. This trend might decrease his output.

The last independent variable is management attributes/levels of the household head. It is significant with p<0.05 and t statistics (2.484) while B value (153.401) is positive which shows that 1 unit increase in management level will increase the total income by an amount of Rs. 153.401. Tolerance and VIF values for model are all satisfactory as value of Tolerance should be greater than 0.1 and VIF should be less than 10.

Logit Model has been used here to gauge the probability of the respondents being poor having borrowed from the Islamic microfinance institutions.

Table 5: Logit analysis    
Model β P-Value
(Constant)  -1.187 .604
Age  -.105 .022**
Gender  -.034 .954
Family size  1.514 .000*
Clients’ Management attributes  -.258 .000*
Family involved .359 .533
Employed someone else -1.289 .132
Loan amount -.465 .237
Income -1.313 .000*
LR chi2 = 88.06

Prob > chi2 = 0.0000

Pseudo R2 = 0.4608

Dependent variable: Poverty

* Statistically significant at less than 1% level based on two-tailed tests.

** Statistically significant at less than 5% level based on two-tailed tests.

*** Statistically significant at less than 10% level based on two-tailed tests.

UNCDF (2004) has taken four factors to measure the impact of Islamic microfinance on clients. One of them is poverty reduction. This report shows positive results in terms of poverty reduction. Here, the b values in the equation represent the change in the logit of the dependent variable with a unit change in the independent variable. The logit of the dependent (outcome) variable is simply the natural logarithm of the probability of Y occurring.  The odds ratio ‘coef’ can be understood as a change in odds of the outcome to occur. The likelihood ratio chi-square of 88.06 with p-value of 0.0000 suggests that our model, as a whole, fits significantly. The value of Pseudo R2 is 0.4608 shows that our predictors account for 46.08 percent of change in our outcome.

Coefficient of Age is negative (-.105) and is statistically significant (p=0.022) at less than 0.05.  One unit increase in the age (year) decreases household’s odds of being in poverty by 0.022 times with all other predictors constant. Which means increase in age leads to lower the poverty level while there is more probability of being poor when the person is of less age. Datt and Jolliffe (1999) and Chaudhry (2009) also reported that demographic factors matters while determining the wellbeing of the people. Gender is not significant with p value greater then 0.05 (p=.954).

Family size is highly significant at 0.01 (p=0.000). Its positive coef value (1.514) tells that increase in family size increase the odds of being poor. One unit increase in the family size increases household’s odds of being in poverty by 1.514 times with all other predictors constant. The smaller the family size, the less are the odds of household being poor. It is consistent with the result of Chaudhry (2009) who stated that chances of an individual or the household to be in poverty increases if the size of household is large.

Management attributes/level of the household is highly significant with p-value of .000 (p=.000(p<0.01) and its negative coef. value (-.258) suggests that if there is one unit increase in the management level, it will decreases household’s odds of being in poverty by 0.258 times with all other predictors constant. Family involved, Employed someone and Loan amount are insignificant with p-values of .533, .132 and .237 respectively. On the other hand income is significant at 0.01 (p=.000). Its negative b coefficient reflects that one unit increase in income level will decrease the odds of poverty to occur by 1.313 time with all other predictors kept constant.

5  Conclusions

It is evident that there is no single answer for the impact of Islamic microfinance activities on poverty. Islamic Microfinance activities have, definitely, affected positively the lives of poor. This positive impact is depicted in the obvious positive differences in income and the resultant expenditure before and after taking loan. Though, in few cases this impact is not significant or limited. Differences in assets possession, health and children education expenditure, before and after taking loan have highlighted the positive effect of Islamic microfinance on the livelihood of poor. This study has identified that Islamic micro financing through assets can be very successful. Our collective findings suggest that Islamic microfinance institutions are not gender biased. It could be concluded that loans should be provided in small amounts over the period of time in the form of installments as smaller loans increase the level of income. The more the loan amount, the higher is the chance of spending it somewhere else. Moreover, providing the clients with training and asking them for book keeping bring positive results. Furthermore, Islamic microfinance creates value to promote economic and social development, employment and growth through the support of micro-entrepreneurs and small businesses while creating relationship-based depository for all such as, the industry, the government and the society.

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Retrieved June 12, 2012, from http://web.undp.org/execbrd/pdf/Main%20Report.pdf

Appendix-1Poverty assessments of three samples out of actual sample

Sample of 30% respondents.
Sr.

No.

FGT of 30% sample of our sample National poverty line Calories based poverty

line

International poverty line

(1.25$)

Before After Before After Before After
1. Head-count Index 24 0 45 25 95 29
2. Poverty Gap Index 4 0 9 5 39 6
3. Squared Poverty Gap

Index

1 0 3 1 19 2
Sample of 33% respondents.
Sr. No. FGT of 33% sample of our sample National poverty line Calories based poverty

line

International poverty line

(1.25$)

Before After Before After Before After
1. Head-count Index 24 0 47 24 95 28
2. Poverty Gap Index 5 0 10 5 40 5
3. Squared Poverty Gap

Index

2 0 3 1 20 2
Sample of 35% respondents.
Sr. No. FGT of 35% sample of our sample National poverty line Calories based poverty

line

International poverty line

(1.25$)

Before After Before After Before After
1. Head-count Index 26 0 47 26 95 29
2. Poverty Gap Index 6 0 10 5 41 6
3. Squared Poverty Gap

Index

2 0 3 1 20 2

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