Grameen microfinancing and poverty alleviation in bangladesh: evidences uses of the foster, greer, and thorbecke indexe and multiple logistic regressions



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GRAMEEN MICROFINANCING AND POVERTY ALLEVIATION IN BANGLADESH: EVIDENCES USES OF THE FOSTER, GREER, AND THORBECKE INDEXE AND MULTIPLE LOGISTIC REGRESSIONS
Abul Bashar Bhuiyan*, Chamhuri Siwar, Abdul Ghafar Ismail, Basri Abdul Talib & Jamaliah Said
*Corresponding and presenting author’s Name: Abul Bashar Bhuiyan & and Email Address: bashariuk@gmail.com

Authors Address:

  1. Dr. Abul Bashar Bhuiyan: Senior Lecturer at the School of Business Innovation and Technoprenuership, University Malaysia Perlis (UniMAP), Perlis, Malaysia & Research Fellow at Accounting Research Institutes (ARI),University Technology Mara(UiTM), Sha-Alam, and Institute for Environment and Development (LESTARI), Universiti Kebangsaan Malaysia (UKM), Bangi- 43600, Selangor, Malaysia. E-mail: bashar@unimap.edu.my




  1. Chamhuri Siwar: Professor Emeritus & Senior Research Fellow at Institute for Environment and Development (LESTARI), Universiti Kebangsaan Malaysia (UKM), Bangi - 43600, Selangor, Malaysia E-mail: csiwar@ukm.my




  1. Abdul Ghafar Ismail: Research Centre for Islamic Economics and Finance, Universiti Kebangsaan Malaysia (UKM), Bangi - 43600, Selangor, Malaysia. Email : agibab@ukm.my; agibab62@gmail.com




  1. Basri Abdul Talib: is an Associate Professor at the Faculty of Economics and Business Universiti Kebangsaan Malaysia (UKM), Bangi - 43600, Selangor, Malaysia E-mail: basri@pkrisc.cc.ukm.my




  1. Dr Jamaliah Said , Assoc professor and Deputy Director (Research & Networking) Accounting Research Institute Universiti Teknologi MARA, 40450 Shah Alam Malaysia, Email: lia533@yahoo.com


ABSTRACT

The study investigates the impact of microcredit on poverty alleviation of the borrowers of the Grameen Bank microfinance scheme in Bangladesh. Towards the achievement of its objectives, the present study has used descriptive statistical and econometric techniques as well as Foster, Greer, and Thorbecke poverty measurement indexes. The Foster, Greer, and Thorbecke indexes was used to assess the incidence of poverty by comparing before-after situations and the multiple logistic regression model was used to estimate the parameters that influenced the position of poor and non-poor borrowers. The study found that Grameen Bank credit has contributed towards reducing the incidence of poverty, poverty income gap and severity of poverty of the borrowers. Moreover, multiple logistic regression model output also shows that there are significant positive impacts of Grameen microcredit for changing the status of poor to non-poor borrowers. The present study recommends policy considerations for the successful and effective operation of microfinance programs through the increase of proper income generating activities, sufficient amount of access to credit, creation of self-employment opportunity for poverty alleviation in Bangladesh.
Keywords: Microfinance, Grameen Bank, Poverty Alleviation, & Bangladesh
JEL classification: G2, N3


  1. INTRODUCTION

The condition of poverty and the number of bordering poor people is increasing in every year since independence in Bangladesh. There were 78.2 million deprived people in 1970 to 80.46 million people in 2009 in Bangladesh ((Imai and Azam, 2010, Islam, 2009)Imai and Azam, 2010, Islam, 2009). Moreover, the studies by Bangladesh Bureau of Statistics (BBS) documented that poverty head count ratio dropped from 58.8 % in 1991 to 48.9 % in 2000 and its further deteriorated to 40.0 % in 2005 (Imai and Azam, 2012, Bhuiyan et al., 2012). In the same way in case of poverty the poverty gap ratio it has condensed from 17.2 % to 12.9 % and the squared poverty gap ratio from 6.8 % to 4.6 % during the same period from 2000 to 2005 respectively. On the other hand, the absolute number of people existing further down poverty mark was in circumstance on the rise a stunning 56 million people were found to be poor in 2005. The conforming number was 55 million in 2000. By the same token, hard core poverty leftovers just about same throughout the era 18.8 % in 2000 and 18.7 % in 2005 respectively (Bhuiyan et al., 2012, Imai and Azam, 2010).
Through the hands of Professor Mohammad Yunus, the microfinance is commonly ostensible as a functional and a pretty process for providing the deprived approachability to access to credit, henceforward dropping poverty and succeeding a sustainable livelihood. In many ways since its conception, the idea of microfinance has still been failed in dipping the overall poverty level in Bangladesh (Amin et al., 2003, Bhuiyan et al., 2012). Similarly, many studies have found that the interest rate microfinance institutions (MFI’s) charge, ranging from 15 to 20 percent of institutional cases, and 33 to 120 percent for non-institutional cases, as one of the major barriers behind reaching a finance solution for the poor in Bangladesh (Kabeer, 2001, Amin et al., 2003). In responses to the above issues, the study aims to assess the contributions Grameen Bank (Pioneer of MFIs in Bangladesh) on the poverty alleviation of the borrowers in Bangladesh.


  1. REVIEW OF LITERATURE

There are numbers of empirical studies have observed into the contributions of microcredit on the poverty alleviations around the world. The present study has gone through the relevant empirical literature to justify the rationality of the objectives to measure the impacts of Grameen Bank on poverty alleviation in Bangladesh. Hossain has leaded a prompt study in 1984 found that both per capita income and household income were absolutely improved through the extent of credit gained from Grameen Bank. On the other hand in 1988 he found that about 91% of Grameen Bank’s participants enhanced their income as well as consumption once joining Grameen Bank (Hossain, 1988). However, Sharma, M. and M. Zeller in 1999 provides evidence that branches tend to be located in poor pockets of relatively well-developed areas than in remoter, less developed regions. Client density of established branches does not exhibit such a feature and actually tends to be better in less advantageous locations (Sharma and Zeller, 1999).


In the same way, Khandker in 2003 found that the microcredit program has special effects on local economies and increasing local village welfare. He also discoveries that micro-finance supported to shrink thrilling poverty more than reasonable poverty at the village level but aggregate poverty dropping effects was not pretty significant. Moreover, Amin, S., A. S. Rai, et al. in 2003 evaluates whether microcredit programs such as the popular Grameen Bank reach the relatively poor and vulnerable in two Bangladeshi villages. They find that while microcredit is successful at reaching the poor, it is less successful at reaching the most vulnerable. The study also suggests that microcredit is unsuccessful at reaching the group most prone to destitution, the vulnerable poor (Amin et al., 2003).

Moreover, Khandker, S. R. in 2005 examines the effects of microfinance on poverty reduction at both the participant and the aggregate levels using panel data from Bangladesh. The results suggest that access to microfinance contributes to poverty reduction, especially for female participants, and to overall poverty reduction at the village level. The microfinance thus helps not only poor participants but also the local economy (Khandker, 2005). On the other hand, Dowla, A. in 2006 observed that how Grameen Bank created social capital by forming horizontal and vertical networks, establishing new norms and fostering a new level of social trust to solve the collective action problems of poor people's access to capital. However, Mahjabeen, R. in 2008 examines the welfare and distributional implications of microfinance institutions (MFIs) in Bangladesh in a general equilibrium framework. The major findings are that MFIs raise income and consumption levels of households, reduce income inequality and enhance welfare. This implies that microfinance is an effective development strategy and has important policy implications regarding poverty reduction, income distribution and achievement of millennium development goals (MDGs)(Mahjabeen, 2008).



In the same way, Berhane, G. and C. Gardebroek in 2011 observed on two key poverty indicators of the access to microfinance, household consumption and housing improvements. They find that borrowing credit indeed causally increased consumption and housing improvements. A flexible specification that takes into account repeated borrowings also suggests that borrowing has cumulative long-term effects on these outcomes, implying that short-term impact estimates may underestimate credit effects (Berhane and Gardebroek, 2011). On the other hand, Ghosh, S. and E. Van Tassel in 2011 examine a one-period model in which poverty minimizing microfinance lenders must raise external funding from a profit maximizing investor. Assuming that the lenders vary in their operating costs, we find that competition between lenders for external funds can lead to a higher aggregate poverty reduction (Ghosh and Van Tassel, 2011). However, Li, X., C. Gan, et al. in 2011 empirically evaluated the impact of microcredit on household welfare outcomes such as income and consumption in rural China. The empirical results favor the wide belief in the literature that the joining microcredit program helps improve households’ welfare such as income and consumption. Despite the optimistic findings on how microcredit has changed the rural households’ living conditions, our results show that the vast majority of the program participants are non-poor, which casts some doubts on the social potential (such as poverty reduction) of China's microcredit programs (Li et al., 2011b).
However, Imai and Azam in 2012 have applied the treatment effects model and PSM to each cross-sectional component of the panel data shows that the poverty reducing effect of MFI on poverty was significantly reduced over the years. They suggests the need of more attention to be drawn to the primary purpose of microcredit, that is, poverty reduction, and also to monitoring loan usages in the situations where the profits of MFIs became increasingly squeezed and their activities became more commercialized under severe competitions among MFIs in recent years (Imai and Azam, 2012).
Furthermore, Alam Saad in 2012 find that female borrowers are better able to allocate their income toward goods more valuable to them and make major household decisions when their income increases. This serves as evidence of increased empowerment or bargaining power of rural women in Bangladesh (Alam, 2012). However Chowdhury, T. A. and P. Mukhopadhaya in 2012 also find that microcredit are more effective in enhancing the economic wellbeing of the poor as well as contributing more in the social aspects of wellbeing. The findings also revealed that, microcredit is improving living standards of the rural poor which contradicts with the existing literature of poverty reduction projects in developing countries like Bangladesh (Chowdhury and Mukhopadhaya, 2012). In the same way Imai, K. S., R. Gaiha, et al. in 2012 found that microfinance significantly reduces poverty at macro level and thus reinforce the case for channeling funds from development finance institutions and governments of developing countries into MFIs (Imai et al., 2012). Moreover, Salim, M. M. In 2013 has structurally estimated profit and impact functions, my MSM estimates suggest that pure profit maximization cannot explain the branch placement pattern for Grameen Bank or BRAC: they both deviate towards poverty alleviation. Targeting one higher standard deviation of poverty headcount costs Grameen 35.2% of its potential profits and BRAC 51.4% (Salim, 2013).


  1. METHODOLOGY

The study employed quantitative research approach towards the achievement of its objectives. The descriptive statistical and econometric techniques as well as Foster, Greer, and Thorbecke poverty measurement indexes used to analyse field survey data of existing microcredit borrowers of Grameen Bank in Bangladesh. The Purposive stratified random sample methodology was used to select samples of respondents. There are 225 samples have been collected from the Grameen Bank microcredit scheme in the area of Sylhet and Chittagong Division in Bangladesh. The present study has been used prominently poverty measurement index which has made by Foster, Greer, and Thorbecke (FGT index) for the assessment of the incidence of poverty of the respondent household on the basis of three dimensions of poverty i.e. (i) incidence of poverty, (ii) intensity of poverty, (iii) severity of poverty in comparison to before and after situation of Grameen Bank borrowers.


For the measurement of incidence of poverty

(1)

For the measurement of intensity of poverty



(2)

For the measurement of severity of poverty



(3)
Where, z is specified poverty line income, N is the number of people in an economy, H is the number of poor (those with incomes at or below z), Yi are individual incomes and α is a "sensitivity" parameter. If α is low, then the FGT metric weights all the individuals with incomes below z roughly the same as well. If α is high, those with the lowest incomes (farthest below z) are given more weight in the measure. The higher the FGT statistic, the more poverty there is in an economy. The most popular application of this index are α is set for 0, 1 and 2 respectively.
Furthermore, the logistic regression model which was constructed to explain how microcredit and other socioeconomic and demographic factors are affecting on the status of poor or non-poor of borrower households after access of microcredit involvement. Moreover, the logistic regression model is a special form of the general log-linear model, which has been become increasingly popular for the categorical data analysis where the dependent variable is categorical (nominal or non-metric) and the independent variables are metric. The poor and non-poor households have measured on the basis of per capita income. Especially those households have daily per capita income above $1.25 in 2009 and $1 in before five years back are considered as non-poor and vice versa for poor households.
There are several numbers of studies used the multiple logistic regression model in Bangladesh to assess the effect of the microcredit program on loan utilization, awareness towards the living-standards and women empowerment, had found positive effects (Zinman, 2010). Different studies used different dichotomous dependent variables in the logistic regression model. Rodriguez, A. G. and S. M. Smith in 1994 used logistic regression analysis to estimate the effects of variables on urban, rural, farm, and nonfarm poverty among households in Costa Rica. Results showed the complexity of the issue, and implied policies to expand education through the secondary level, to create more opportunities for rural off-farm employment (Rodriguez and Smith, 1994). Flum et al., 2005 in 2005 identified the dominant factors necessary for the sustained participation of farmers; logistic regression analyses were performed(Flum et al., 2005). More studies on Logistic Regression Model: (Serrano-Cinca and Gutiérrez-Nieto, 2012, Rosenberg et al., 2011, Panda and Atibudhi, 2011, Dukic et al., 2011, Ahmed, 2011, Van Gool et al., 2009, Panda, 2009b, Panda, 2009a, Twine et al., 2007, Arsyad, 2006, Holvoet, 2004).

The logistic regression model for Grameen Bank respondents:



………………. (4)

Where,


Pi = 1 if the households “Non poor”

1-Pi= 0 if poor

X = the list of explanatory variables
L = βο1 X12 X23X3 + β4X4 + β5X5+ β6X6 + β7X7+ β8X8+ β9X9 + β10X10 + u
Where are,

L =Household poor and non-poor status (Dummy variables where non poor =1, poor =0)

X1 = Total amount of Loan Received, X2= Borrower Age (on January 2009), X3 = Borrowers no education (1= No education and 0= Otherwise), X4= Borrowers within primary education (1=up to primary education and 0= Otherwise),X5=Respondent Occupation with Agriculture (1=With Agriculture and 0= Otherwise), X6=Respondent Occupation with Business (1=With Business and 0=Otherwise X6=Total Household Size, X8 =Total Household Earning Members, X9 = No. of loan X10 = Income from other sources

u = Error term βο = Constant (intercept term) β1,2…10 are the coefficients of explanatory variables



4.0 FINDINGS AND DISCUSSION

4.1. The Contributions of Microcredit on Poverty Alleviation

The prime aim of this study is to assess incidence of poverty Grameen Bank respondents’ households. Based on Foster, Greer, and Thorbecke (FGT) index, the study measured poverty in three aspects i.e. (i) incidence of poverty, (ii) intensity of poverty, (iii) severity of poverty. From the tables 01 and 02 as well as Figures 1.1, the study show that the present incidences of poverty in Grameen Bank’s respondents’ households is 68% while it was 71% five years ago. The finding indicates that 68% of Grameen Bank’s respondents’ per capita income was under $1.25 in 2009 and about 71% of Grameen Bank’s respondents’ households’ per capita income was below $1 before joining the credit programme. In comparing the study’s results, Grameen Bank respondents reducing the incidence of poverty by 3% during five years period. Thus, the study output reveals that microcredit has contributed well in reducing the incidence of poverty but it is not at the significant and remarkable level.

Table 01 Distribution of Poverty status of Grameen Bank respondents Households (at present and five years back)

Poverty status of Grameen Bank respondents Households

Indicators

Grameen Bank

At present

Before

Incident of poverty (%)

68

71

Intensity of poverty (%)

26

27

Severity of Poverty (%)

13

14

Source: Primary Data from Survey

Figure 1.1 Distribution of the present poverty status of Grameen Bank respondent’s households.

Source: Primary Data from Survey

In terms of Poverty gap or intensity of poverty, 26% of Grameen Bank respondents’ households are still making distance of poverty line per capita income from the poverty line standard which is also called average poverty gap, or the amount of income necessary to bring everyone in poverty right up to the poverty line, divided by total population but it was 27% over the last five year period. On the other hand, 26% of Grameen Bank respondents are in the poverty gap at present and 27% five years ago. The study indicates that if Grameen Bank borrowers are able to increase 26% of their per capita income then poverty would be fully alleviated. However Grameen Bank respondents reduce intensity of poverty by 1% over five year’s period accordingly.

Table 02 Distribution of reduction of poverty after involvement with credit

Reduction of poverty

Indicators

Grameen Bank

Incident of poverty (%)

3

Intensity of poverty (%)

1

Severity of Poverty (%)

1

Source: Primary Data from Survey
In case of the square of poverty gap or severity of poverty, at present 13% of Grameen Bank respondents’ household are living in the situation of the square poverty gap or severity of poverty where it was 14% over five years ago. In the same way, in comparing both MFIs’ respondents, Grameen Bank respondents have reduced severity of poverty by 1% by Grameen Bank respectively.

4.2 Multiple Logistic Regressions on Status of Poor and Non-Poor

The logistic regression model was constructed to explain how microcredit and others socio-economic and demographic factors are affecting the status of poor or non-poor borrowers’ households after access to microcredit. The status of poor or non-poor of borrower’s is considered as the depended variable and total amount of loan received considered as the independent variables including nine others demographic and socioeconomic factors especially age, borrowers within primary education, borrowers above primary education, respondent occupation with agriculture, respondent occupation with business, total household members, total household earning members, number of loan and income from the other sources. The model considers 255 survey respondents of Grameen Bank households from the Sylhet and Chittagong division in Bangladesh. The poor and non-poor households were measured on the basis of per capita income. Households that have daily per capita income above US $1.25 are considered as non-poor and vice versa for poor households.

Table 03 the result of logistic regression analysis of poor and non-poor status for Grameen Bank Respondents

Variables 

B

S.E.

Wald

Sig.

Exp(B)

Constant()

-.413NS

1.986

.043

.835

.662

Total Amount of Loan Received

.0001***

.00002

21.259

.000

1.000

Borrower Age (on January 2009)

.085***

.037

5.461

.019

.918

Borrowers within primary education

1=within primary education

0=Otherwise


2.643**

1.144

5.340

.021

14.050

Borrowers above primary education

1=Above primary education

0=Otherwise


1.679NS

1.054

2.540

.111

5.362

Respondent Occupation with Agriculture

1=Agriculture

0=Otherwise


-.927NS

.666

1.937

.164

.396

Respondent Occupation with Business

1=Business

0=Otherwise


.843NS

.914

.852

.356

2.324

Total Household Size

-2.101***

.406

26.833

.000

.122

Total Household Earning Members

2.365***

.613

14.862

.000

10.642

No. of loan

-.623***

.220

7.992

.005

.537

X10 Income from the other sources

.001***

.00014

28.381

.000

1.001

Chi-square= 231.753, Wald Chi-Square= 29.724, Cox and Snell R- Square= 0.597, Log Likelihood= 90.026a and Nagelkerke R- Square. = 0.833

Note: *** Indicate significant at 99%, ** Indicate at 95% and NS Indicate at 90% level respectively.

Source: Primary Data from Survey in 2009


The overall result of this logistic regression model is strongly supported from the value of the Cox and Snell R2 are 0.597 and Cox and Snell R2 Nagelkerke are about 0.833 which is to be 0.000 level of significant measuring the goodness of fit of the model. The findings shows that there are significant relationships between Grameen Bank microcredit and improvement of poverty status of the respondents’ household including other socio-economic and demographic characters including achieving of education, operating income generating activities, total number of earning members, borrowing credit and income from others sources. Moreover, Abur and Torruam in 2012 used logistic regression on poverty and found significant influenced of microcredit on the reduction of poverty (Abur and Torruam, 2012). However, On the other hand, Li et al. in 2011 also used logistic regression for examined factors are identified as determinants in households’ access to microcredit, including educational level, household size, and income among others. The empirical analysis established a positive relationship between households’ credit demand and access to credit (Li et al., 2011a).

Moreover, the variable of the borrower’s total amount of credit has shown statistically positive significant (β= .0001 & P>.0001) relation on the status of poor and non-poor of Grameen Bank borrowers. It is mean that borrower’s total amount of credit has strongly influenced to increase opportunity of income generating activities and decrease the status of poor into non-poor category respectively. Furthermore, the variable age of respondents of Grameen Bank shows positive effect at the significant level (β= .085 & P> .019) on the status of poor and non-poor. The study also found that most of the respondents about 56% of their age within 35 years old participating in the microcredit programmes in Grameen Bank. Thus, study summarise that age is considered as an important factors for poor borrowers in income generating activities by using credit properly reducing poverty accordingly.

In the same way Imai, K. S., R. Gaiha, et al. in 2012 found that amount of credit significantly reduces poverty at macro level and thus reinforce the case for channeling funds from development finance institutions and governments of developing countries into MFIs (Imai et al., 2012). However Chowdhury, T. A. and P. Mukhopadhaya in 2012 also find that microcredit are more effective in enhancing the economic wellbeing of the poor as well as contributing more in the social aspects of wellbeing (Chowdhury and Mukhopadhaya, 2012). In the same way, Imai and Azam in 2012 have applied the treatment effects model and PSM to each cross-sectional component of the panel data shows that the MFI effect of reducing of poverty at significantly over the years. (Imai and Azam, 2012). Furthermore, Li, X., C. Gan, et al. in 2011 empirically evaluated the impact of microcredit on household welfare outcomes such as income and consumption in rural China. The empirical results favor the wide belief in the literature that the joining microcredit program helps improve households’ welfare such as income and consumption (Li et al., 2011b).

Grameen Bank microcredit respondents within primary education show significant positive relationship (β=2.643 & P>.021) on the status of poor and non-poor. On the other hand, the respondent’s education up to primary level and above also shows positive coefficient (β=1.679 & P>.111) on the status of poor and non-poor. Thus, these findings revealed that respondents who have received high level of education are likely to make more income in their family and move out from poverty compared to respondents who do not have primary education. So it can be concluded that higher educated respondents are able to lead high livelihood status in Bangladesh. However, the Grameen Bank respondents’ occupation in agriculture has negative coefficient (β= -.927 & P>.164) but statistically not significant in the status of poor and non-poor. On the other hand, respondent’s occupation in business or income generating activities (IGAs) shows positive coefficient (β=.843 & P>.356) on the status of poor and non-poor. The study output revealed that respondents who have invested their borrowed credit in a business or any form of income generating activity, they are earning more income rather than agriculture or other occupations. On the other hand, Rodriguez, A. G. and S. M. Smith in 1994 used logistic regression analysis to estimate the effects of microcredit on urban, rural, farm, and nonfarm poverty among households in Costa Rica. Results showed the complexity of the issue, and implied policies to expand education through the secondary level, to create more opportunities for rural off-farm employment (Rodriguez and Smith, 1994).

Total number of household members, Grameen Bank respondents shows statistically significant (β= -2.101 & P>.0001) negative influence on the status of poor and non-poor. On the other hand, respondents’ earning family members as a determinant has positively influenced (β=2.365 & P>.0001) the status of poor and non-poor of Grameen Bank microcredit borrowers. The study result indicates that the Grameen Bank respondents’ households have less opportunity to engage their family members for income generating activities but those families have more number of earning members in income generated activities then they moving from the status of poor and non-poor respectively. Moreover, Stiglitz. P in 1999 also find that increase household size will lead to increase in dependence rate, which may result to increase poverty (Stiglitz, 1999).

Moreover, the number of loan, has also shown negative influence but not in the significant influenced (β= -.623 & P>.005) level on the status of poor and non-poor of Grameen Bank respondents. The output indicates that the borrowers who increased their family income those who have taken large amounts of loan rather than more number of loans from MFIs and used that amount of loan in income generating activities. Finally, the monthly income of other sources of household has also shown statistically positive significant (β= .0001 & P>.0001) influence on the status of poor and non-poor of Grameen Bank respondents. The findings also indicate that income from other sources also influences on the total income of family and helps to improve standard of living in the sustainable stage. It also revealed that other earning members of the family have also strong contribution to the family’s livelihood to move out from poverty compared to income from microcredit respectively.



5.0 CONCLUSION

The study aims to assess the impact of microcredit on poverty alleviation. This study has conducted assessment of poverty in three aspects i.e. (i) incidence of poverty, (ii) intensity of poverty, (iii) severity of poverty. The result indicates that the present incidence of poverty of Grameen bank households are 68% compared to 71 % in the last five years ago. Moreover, in case of intensity of poverty the Grameen Bank respondent’s status is 26% and 27% compared to five years ago. On the other hand, square poverty gap or severity of poverty, at present 13% of Grameen Bank respondents’ households are living in the situation of the square poverty gap or severity of poverty at 14% compared five years ago. Moreover, in comparing the results of poverty on the basis of the above three dimensions, Grameen Bank respondents have reduced poverty status only by 3% for incidence of poverty, 1% for intensity of poverty and 1% for severity of poverty over the five years period accordingly. Thus, study output reveals that microcredit is contributing to reduction of poverty status among respondents of the Grameen Bank but in the impressive progress.

The summarized findings reveal that overall estimated result of logistic regression analysis of the Grameen Bank microcredit borrowers showed an acceptable coefficient between dependent and explanatory variables where the Cox and Snell R2 are 0.597 and Cox and Snell R2 Nagelkerke are 0.833 which is at 0.000 level of significance. This proves the goodness of fit of the model. The R² value indicates that the status of poor and non-poor respondents’ households could be explained by all the independent variables in the model. Thus, the study concludes that there is significant relationship between reduction of household’s status of poverty and microcredit loan of Grameen Bank respondents including other socio-economic and demographic characters. Finally, from the above findings, the study revealed that all the explanatory variables are influencing the reduction of poverty status of the respondents of Grameen Bank especially in achieving education, operating income generating activities, total number of earning members, borrowing credit and income from other sources to improve the livelihood status from the poverty level.

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