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\title{Impact of Micro-Credit Management System of an NGO in Socio-Economic Development of Rural Women in Perspective of Bangladesh}
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             \author[1]{M. M. Israfil Shahin  Seddiqe}

             \author[2]{Benojir  Ahammed}

             \affil[1]{  Kushtia Polytechnic Institute, Bangladesh.}

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\date{\small \em Received: 11 April 2015 Accepted: 3 May 2015 Published: 15 May 2015}

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\begin{abstract}
        


Bangladesh is a developing country and most of the people live in under the poverty line. As a non-profit sector of a country, beside Government Organizations (GO?s), Non- Government Organizations (NGO?s) are truly vital cog in the paradigm of social development in Bangladesh. It is generalized that Civil Society Organizations (CSO?s) and NGO?s have created jointly a new era of partnership in poverty alleviation activities, gender sensitive issues, education, health and been giving competent sustainability in the socioeconomic conditions of Bangladesh. Since the NGO?s are aimed to spreading awareness in people, strengthening and institutionalization of democracy promotion as well as ensuring maximum people participation in community driven development approach. ?Jagorani chakro? is one of the wellknown and dedicated NGO?s in Bangladesh. The paper represents the microcredit system of this NGO?s and how the rural women play an important role in their family and society using microcredit loan.

\end{abstract}


\keywords{non-government organization (NGO), social development, microcredit system.}

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
lobal Partnerships (GP) expands opportunity for people living in poverty by supporting microfinance and other sustainable solutions in Bangladesh. Microfinance is the provision of financial services to low-income clients or solidarity lending groups including consumers and the self-employed, who traditionally lack access to banking and related services. Microfinance is a broad category of services, which includes microcredit. Microcredit is provision of credit services to poor clients. Due to the broad range of microfinance services, it is difficult to assess impact and very few studies have tried to assess its full impact.\par
Bangladesh is regarded as less developed country in the world with huge unemployment, over population and poverty problem. The economy of our country is agro base, which heavily depends on the will of nature. There are 157,832,267 (estimated, 2011, Bangladesh Demographic Profile 2011) peoples in our country but only 56.8\% (Male 61.3\% and Female52.2\%) (Wikipedia) people are literate of the total population (UNESCO Institute for Statistics. 2012). In our country a good number people of the villages are unemployed and around 84.0\% people lived low standard. For their problems, some government and non-government organization established and these organizations try to maintain their duty with improving the economic condition of the poor people as landless labour, low level income holders and landless people. They also teach how a man can live a consciousness and luxurious life.\par
A good number of people in our research area are poor and most of them are taking loan on the NGO's to improve their standard of living as economical side. In our research we construct an analysis of the NGO's where they taking loan, form such kind of NGO like "Jagorani chakro" in our country. If my analyzing report comes in any of their use our effort will be successful.\par
Jagorani Chakra Foundation (JCF) was established in 1976 as a national social welfare organization working mainly in the southwest region of Bangladesh and it works with full reputation and cordially to develop the socio-economic development. Jagorani Chakra Foundation has a vision of socially; economically sustainable and environmentally balanced communities, where poverty has been overcome and people live in dignity and security. The goal of Jagorani Chakra Foundation is to support the process of poverty alleviation and the creation of socio-economic equity through the promotion of sustainable community based people's institutions in alliance with Government and other agencies and projects. The main objectives of "Jagorani chakro" are as follows-\par
? HID, capacity building and sustainability 
\section[{? Human Rights and Social Justice}]{? Human Rights and Social Justice}\par
? Organizational Sustainability. 
\section[{II.}]{II.} 
\section[{Literature Review}]{Literature Review}\par
A variety of literature exists on Micro-credit Management System in Bangladesh. In international developments; the concept of microfinance has become increasingly important over the last three decades or so. Microfinance is defined as the practice of 'supply of loans, savings, and other basic financial services to the poor' (Consultative Group to Assist the Poor [CGAP], 2003). \hyperref[b5]{Greene and Gangemi (2006)} place microcredit at the heart of microfinance, where it is widely understood as 'the practice of offering small, collateral-free loans to members of cooperatives who otherwise would not have access to the capital necessary to begin small businesses'  {\ref (Hossain, 2002: 79)}.\par
Recent literature has highlighted increasing recognition of microfinance as a crucial tool in the alleviation of poverty, via its creation of employment opportunities and promotion of rural development and fulfillment of creative potential \hyperref[b8]{(Navajas et al., 2000;}\hyperref[b0]{Ahmad, 2001}\hyperref[b1]{Ahmad, , 2002;;}\hyperref[b16]{Yunus, 2001;} {\ref CGAP, 2003;}\hyperref[b13]{Brau and Woller, 2004;}\hyperref[b7]{Lashley, 2004;}\hyperref[b4]{Chowdhury et al., 2005)}. Interest in microfinance has been fuelled by its acceptance and successful implementation in organizations such as ACCION in the US; BancoSol in Bolivia; Association for Social Advancement (ASA) and Bangladesh Rural Advancement Committee (BRAC) in Bangladesh; and Bank Rakyat Indonesia (BRI) in Indonesia \hyperref[b2]{(ASA, 1997;} {\ref Navajaset al., 2000)}. One of the most notable innovations in microcredit is the Grameen Bank in Bangladesh which was founded by Muhammad Yunus in 1983, in response to the numerous challenges he experienced in attempting to lend to the poor via existing commercial banks. Microcredit has since been replicated in more than 65 countries throughout the world. The year 2005 was declared the 'International Year of Microcredit by the United Nations'. The clearest official recognition thus far of the impact of microfinance has been the award of the 2006 Nobel Peace Prize to Muhammad Yunus and the Grameen Bank (The Nobel Foundation, 2006). However, the numerous accolades of microfinance have been countered by heavy criticism in the literature with regard to high interest rates, exploitation of women, loan repayment, unchanging poverty levels and failure to cater effectively to the target groups \hyperref[b9]{(Holt, 1994;}\hyperref[b10]{Dignard and Havet, 1995;}\hyperref[b11]{Christen, 1997;}\hyperref[b12]{Mallick, 2002;}\hyperref[b13]{Brau and Woller, 2004)}.\par
Socio-economic problem itself remains the largest challenge to the social and economic development within the Bangladesh and currently, various Government and Non-government organizations are attempting to tackle this problem in the Bangladesh. This article, therefore, aims to discuss the micro-credit system to develop the socioeconomic condition of rural people. 
\section[{III.}]{III.} 
\section[{Research Methodology}]{Research Methodology}\par
Methodology is must for every kind of research problems. Systematically it is very essential to collect accurate and sufficient data to a good research. The main objective of the research is to find out the true which is hidden and is not been discovered as yet. Each research study has its own specific purpose. 
\section[{a) Defining Population and study area}]{a) Defining Population and study area}\par
Recently there are around 330995 borrowers out of 412157 members of "Jagorani chakro" under 24 districts in Bangladesh (Jagorani Chakra Foundation website). In this research, the people of three villages who are benefited from the credit program of "Jagorani chakro" are the population of this study. For collecting primary data of the research, we select three villages of Jessore district for collecting necessary data of microcredit management system of "Jagorani chakro". There are 2229 male and 2206 female (Census 2011) in these village and around 335 women are taking loan from "Jagorani chakro". If we define the population of all member of "Jagorani chakro" in Bangladesh and select some village for my study then we get a meaningful and real pattern of microcredit management system of "Jagorani chakro". It is very costly and time dependent. For these reason we select the study area as these three villages. 
\section[{b) Sampling and Sample Units}]{b) Sampling and Sample Units}\par
Sampling is essential and important part of statistical survey. Nowadays it is quite impossible to census the whole population of any research. For statistical analysis it is really important to know the sampling frame and sampling technique involved in it because of wrong and misleading conclusions. From several sampling techniques in this paper simple random sampling will be applied for collecting necessary because the simple random sampling procedure is very easy and most acceptable. Using interview method we collect 102 samples out of 335 borrowers by simple random sampling without replacement. We also face several problems in collecting data from the brewers. On the other hand NGO's officials also feel uneasy to disclose some secrete and confidential matters. In fact, data collection from this field was not easy. 
\section[{IV. Data Analysis and Result Discussion}]{IV. Data Analysis and Result Discussion}\par
Generally it is important to know the characteristics or nature of data before performing any statistical analysis. That is why several statistical techniques like frequency distribution, contingency analysis, fitting of an appropriate distribution, test of hypothesis etc. are used to investigate the socioeconomic condition of the borrowers. 
\section[{a) Percentage analysis}]{a) Percentage analysis}\par
At first we present Percentage distribution, and summary statistics of data it is collected from borrowers. It is observed from our data that the highest 99\% of the borrowers are married and only 01\% is unmarried. Also in the married borrowers 5.9\% are widow and 94.1\% are living together in their house. We have already mentioned that the total number of sample observations in our study is 102.  Contingency analysis is designed to test any association between different characteristics that could be useful in the socio-economic condition of the borrowers. For contingency analysis, we assume the hypothesis of independence or homogeneity as the null hypothesis. The expected frequency under the null hypothesis is,N O O E j i ij × =\par
Where, ij E is the expected number of respondents in the (i,j)th cell ; i O and j O represent the number of respondent at the i-th row and j-th column of the contingency table respectively and N is the total number of respondents.\par
From each contingency table examination of association between the components and the various segment of components are made by computing chisquare using the formula given by,) 1 )( 1 ( 2 2 \textasciitilde ? ? ? = ? c r ij ij ij N E O ? ?\par
Where, ij O is the observed number of respondents in the (i,j)th cell, E ij is he expected number of respondents in the (i,j)th cell, r is the number of rows and c is the number of columns.\par
To test any association between different characteristics we define the test hypothesis that, there is no association between two characteristics. 
\section[{Status}]{Status}\par
Test Regression is the most important analysis in statistics analysis. It is important to find the dependence of variable or characteristics. Here all are numerical characteristics so we use liner multiple regression model. We investigate which are most important to increasing the income after taking loan. At first we consider amount of loan, age of the borrowers, earning person in a family, and monthly income before taking loan are mainly responsible for increasing the borrowers income after taking loan and here we use step wise regression model to identify the appropriate variable actually who are responsible for increasing the income after taking loan. For this the multiple regression models are, Y= XB + U Where, Y is dependent variable, X is independent variable, B is unknown parameter, and U is random error terms.\par
i. Assumptions (i) U i \textasciitilde  NID (0,?? 2 ??) (ii) X i 's are fixed over repeated sample.   From the above table , the determination of coefficient (R 2 ) are 0.768(a), 0.783(b), 0.793(c), 0.793(d) i.e. the explanatory variable (Monthly Income before taking loan, Amount of loan, total earning person) can explain 79.30\% of dependent variable (Monthly Income after taking loan). Hence Age does not effect on the regression model. Finally we can say that after income mainly depend on before income and less dependent of amount of current loan and earning person. 
\section[{d) Correlation Analysis}]{d) Correlation Analysis}\par
Correlations of the different characteristics of the borrowers are obtaining the relationship between them.\par
Figure  {\ref 3} : Correlation matrix between income, expenditure and amount of current loan From the above correlation we can see that, Income before loan is positively correlated with income after loan, Expenditure before loan, Expenditure after loan respectively.  Similarly, Income after loan is positively correlated with Income before loan, Expenditure before loan, Expenditure after loan, and also Income after loan is positively with amount of current loan respectively. Expenditure before loan is positively correlated with Income before loan, Income after loan, Expenditure after loan respectively. Expenditure after loan is positively correlated with Income before loan, Income after loan, Expenditure before loan respectively. Amount of current loan is positively correlated with Income after loan, but is correlated and insignificant with income before loan, Expenditure before loan and Expenditure after loan.\par
We have also the correlated value between income, expenditure and amount of loan, which are in the following V. 
\section[{Conclusion and Recommendation}]{Conclusion and Recommendation}\par
At present poverty alleviation is the most important program of GOs (Government Organizations) and NGOs (Non-Government Organizations) of Bangladesh. And there are many NGO's for poverty alleviation and socio-economic development of Bangladesh. Among these NGO's, "Jagorani chakro" is a well Organized and big NGO. In this study the microcredit management system can be analyzed and obtain the impact of micro-credit management system of "Jagorani chakro".\par
At first we present some summary statistics on the socio-economic condition and the micro-credit management system of "Jagorani chakro". We observe that all members are female. Most of them (99\%) are married. They are both Muslim (82\%) and Hindu (18\%). They are not well-educated. Most of them live in Kacca (Soil made) houses and their type of sanitation is not good, which is also Kacca (Soil made). Almost all of them are not involved in any type of formal profession, they are house-wives. The modal age-group of the borrowers is 30-40 years. Their average monthly income before and after taking loans are BDT.4927.00 and BDT.5758.00 and expenditure before and after taking loan are BDT.4139.00 and BDT.4874.00 respectively. They take loan on average BDT.16059.00 from the NGO's. Most of them invest their loan in general (51\%) purpose. Each family of borrowers have on average one mobile after taking loan, that have not before taking loan.\par
In this research, the researcher has found some weakness and deficiency in the NGO's, which are the main weaknesses of NGO's of Bangladesh. Among the weakness of the NGO's loan recovery system is most unscientific. The NGO's recover their loan money in weekly or monthly or totally installment. But in practice there is no scope to earn any profit within a week or to produce goods within a month. So, weekly or monthly loan payment system is not scientific or profitable to the borrowers of NGO's like "Jagorani chakro". The borrowers of "Jagorani chakro" pay the interest in weekly or monthly or totally. The borrowers know they pay the interest 15\% but the NGO's recover their loan money in weekly or monthly or totally installment, as a result the borrowers pay the interest more than 15\% because weekly or monthly loan payment but it is not clear in borrowers. So it is most profitable for NGO's.\par
For taking loan from this NGO's only one condition fulfilled and the condition is joining the NGO's as a member. The borrowers do not face any problem for taking loan from this NGO. The NGO's worker provides the loan with sufficient advice and maximum 72\% borrowers are following their advice. If uncertainly the borrowers does not pay their loan in weekly or monthly they face several problem such as the NGO's worker go to their home, do the bad behave, sometimes sell their cow, goat or other valuable things. It is very bad for the borrowers. As a result they feel mental problem and spoil their renown in the society. In this situation all borrowers will be a member of the NGO's in future and in this purpose they suggest a considerable mind of the NGOs rule and worker.\par
The NGO's taking interest 15\% for the loan in general purpose. The maximum 81\% borrowers think the loan interest is very high and 75\% from them and they want a considerable interest at maximum 4\%.\par
Most of the borrowers (56\%) opinion are taking loan from NGO's is bad but they are taking loan from NGO's for their several problem or do a work at a time which is small costly. And the maximum 37\% borrowers are the member of NGO's by the NGO's worker.\par
In this research most remarkable point is all the poor people are not taking loan from the NGO's and the NGO's do not cover the poorest section rural people in development programs. They are deprived completely from NGO's development programs and become poor to poorer. It creates many problems in the society but NGO's could do nothing for them.\par
In general we can say that NGO's are quite silent about the future of the loaners and at the same about their final target. They also fail to give an account of the future of the poor and the future of the NGO's activities or what may happen when the NGO's will stop their development activities among the poor and powerless people. So, in this purpose the NGO's have a targeted time for development programs and fulfill this target.\par
Anyway, in this study the above difficulties are very complex. We may get clear picture of NGO's microcredit management system through impact the management system of NGO's like "Jagorani chakro".\par
And finally, the NGO's adopt a clear policy for the socio-economic development of the poor as the country. Otherwise it neither may nor eradicate the poverty and social injustice from the society.\par
In this analytical research, it is found that the objectives and programs of NGO's. But in practice the NGO's is better in some aspects in some places and has done badly in some others aspects.\begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-2.png}
\caption{\label{fig_0}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-3.png}
\caption{\label{fig_1}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.6221649484536081\textwidth}P{0.21797680412371132\textwidth}P{0.009858247422680412\textwidth}}
Status\tabcellsep Percentage (\%)\tabcellsep \\
Total\tabcellsep 100.0\tabcellsep \\
Age group\tabcellsep \tabcellsep \\
20-30\tabcellsep 26.5\tabcellsep \\
30-40\tabcellsep 39.2\tabcellsep \\
40-50\tabcellsep 28.4\tabcellsep \\
50-60\tabcellsep 04.9\tabcellsep \\
60-75\tabcellsep 01.0\tabcellsep \\
Education qualification\tabcellsep \tabcellsep \\
Signature\tabcellsep 64.7\tabcellsep \\
Primary Secondary Higher Secondary Family type\tabcellsep 24.5 07.8 02.9\tabcellsep Year 2015\\
Single\tabcellsep 60.8\tabcellsep \\
Combined\tabcellsep 39.2\tabcellsep \\
Number of children\tabcellsep \tabcellsep \\
?02\tabcellsep 41.59\tabcellsep \\
03-04\tabcellsep 50.49\tabcellsep \\
05/05+\tabcellsep 07.92\tabcellsep \\
Idea about family planning and AIDS\tabcellsep \tabcellsep \\
Yes\tabcellsep 63.37\tabcellsep \\
No\tabcellsep 37.63\tabcellsep \\
Relation with householder\tabcellsep \tabcellsep \\
Husband\tabcellsep 89.2\tabcellsep \\
Father\tabcellsep 02.9\tabcellsep \\
Son\tabcellsep 04.9\tabcellsep \\
Others\tabcellsep 02.9\tabcellsep \\
Taking loan from other NGO\tabcellsep \tabcellsep \\
Yes\tabcellsep 30.4\tabcellsep \\
No\tabcellsep 69.6\tabcellsep \\
Householder's occupation\tabcellsep \tabcellsep \\
Agriculture\tabcellsep 31.4\tabcellsep \\
Business\tabcellsep 32.4\tabcellsep \\
Service\tabcellsep 14.7\tabcellsep \\
Labor\tabcellsep 10.8\tabcellsep \\
Others\tabcellsep 10.8\tabcellsep \\
Amount of Current loan (BDT)\tabcellsep \tabcellsep \\
00001-10,000\tabcellsep 48.0\tabcellsep \\
10,001-20,000\tabcellsep 39.2\tabcellsep \\
20,001-30,000\tabcellsep 07.8\tabcellsep \\
30,001-40,000\tabcellsep 01.0\tabcellsep \\
40,001-50,000\tabcellsep 02.0\tabcellsep \\
50,001-60,000\tabcellsep 02.0\tabcellsep \\
Purpose of loan taking\tabcellsep \tabcellsep \\
Business\tabcellsep 24.5\tabcellsep \\
Agriculture\tabcellsep 21.6\tabcellsep \\
General\tabcellsep 51.0\tabcellsep \\
Seasonal\tabcellsep 01.0\tabcellsep \\
Cow\tabcellsep 02.0\tabcellsep \\
Suggested amount of loan interest (BDT)\tabcellsep \tabcellsep \\
1-2\tabcellsep 21.56\tabcellsep \\
2-5\tabcellsep 39.22\tabcellsep \\
5/5+\tabcellsep 20.59\tabcellsep \\
No idea\tabcellsep 18.63\tabcellsep \end{longtable} \par
  {\small\itshape [Note: © 2015 Global Journals Inc. (US)]} 
\caption{\label{tab_0}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{}
\end{longtable} \par
  {\small\itshape [Note: b) Contingency Analysis]} 
\caption{\label{tab_1}Table 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{P{0.2959962756052142\textwidth}P{0.18440409683426442\textwidth}P{0.12900372439478586\textwidth}P{0.09338919925512104\textwidth}P{0.06885474860335195\textwidth}P{0.07835195530726256\textwidth}}
Model\tabcellsep Name of variable\tabcellsep \multicolumn{2}{l}{Values of Coefficient with SE}\tabcellsep Values\tabcellsep Significance\\
\tabcellsep \tabcellsep B\tabcellsep Std. error (SE)\tabcellsep of t\tabcellsep at 10\%\\
1\tabcellsep Constant\tabcellsep 987.903\tabcellsep 335.086\tabcellsep 2.948\tabcellsep 0.004*\\
\tabcellsep Before income\tabcellsep 0.968\tabcellsep 0.053\tabcellsep 18.179\tabcellsep 0.000*\\
2\tabcellsep Constant\tabcellsep 535.097\tabcellsep 368.493\tabcellsep 1.452\tabcellsep 0.150\\
\tabcellsep Before income\tabcellsep 0.960\tabcellsep 0.052\tabcellsep 18.526\tabcellsep 0.000*\\
\tabcellsep Loan amount\tabcellsep 0.031\tabcellsep 0.012\tabcellsep 2.625\tabcellsep 0.010*\\
3\tabcellsep Constant\tabcellsep -308.420\tabcellsep 523.187\tabcellsep -0.590\tabcellsep 0.557\\
\tabcellsep Before income\tabcellsep 0.945\tabcellsep 0.051\tabcellsep 18.404\tabcellsep 0.000*\\
\tabcellsep Loan amount\tabcellsep 0.028\tabcellsep 0.012\tabcellsep 2.393\tabcellsep 0.019*\\
\tabcellsep Total earning person\tabcellsep 634.561\tabcellsep 284.649\tabcellsep 2.229\tabcellsep 0.028*\\
4\tabcellsep Constant\tabcellsep -286.618\tabcellsep 883.558\tabcellsep -0.324\tabcellsep 0.746\\
\tabcellsep Before income\tabcellsep 0.944\tabcellsep 0.052\tabcellsep 18.217\tabcellsep 0.000*\\
\tabcellsep Loan amount\tabcellsep 0.028\tabcellsep 0.012\tabcellsep 2.380\tabcellsep 0.019*\\
\tabcellsep Total earning person\tabcellsep 637.412\tabcellsep 300.801\tabcellsep 2.119\tabcellsep 0.037*\\
\tabcellsep Age\tabcellsep -0.684\tabcellsep 22.285\tabcellsep -0.031\tabcellsep 0.986\\
\multicolumn{6}{l}{* Indicate the test is significant at 10\% level of significance. That means there is dependence between them.}\\
\multicolumn{3}{l}{Then the estimated stepwise regression lines are}\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{2}{l}{Model (a): ?? ? = 987.903+ 0.968X 1}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{Model (b): ?? ? = 535.097+ 0.960X 1 +0.031X 2}\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{Model (c): ?? ? = -308.420+ 0.945X 1 +0.028X 2 +634.561X 3}\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{Model (d): ?? ? = -286.618+ 0.944X 1 +0.028X 2 + 637.412X 3 -0.684X 4}\tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep R\tabcellsep \multicolumn{2}{l}{Determination of coefficient (R 2 ) Adjusted R 2}\tabcellsep \tabcellsep \\
\tabcellsep \multicolumn{2}{l}{0.876(a) 0.768}\tabcellsep 0.765\tabcellsep \tabcellsep \\
\tabcellsep \multicolumn{2}{l}{0.885(b) 0.783}\tabcellsep 0.778\tabcellsep \tabcellsep \\
\tabcellsep \multicolumn{2}{l}{0.891(c) 0.793}\tabcellsep 0.787\tabcellsep \tabcellsep \\
\tabcellsep \multicolumn{2}{l}{0.891(d) 0.793}\tabcellsep 0.785\tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_3}Table 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{20154} \par 
\begin{longtable}{}
\end{longtable} \par
 
\caption{\label{tab_4}Year 2015 Table 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{5} \par 
\begin{longtable}{}
\end{longtable} \par
 
\caption{\label{tab_5}Table 5 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{:} \par 
\begin{longtable}{P{0.3078740157480315\textwidth}P{0.1405511811023622\textwidth}P{0.12047244094488188\textwidth}P{0.12381889763779526\textwidth}P{0.10374015748031497\textwidth}P{0.05354330708661417\textwidth}}
\tabcellsep Income\tabcellsep Income after\tabcellsep Expenditure\tabcellsep Expenditure\tabcellsep Current\\
\tabcellsep before loan\tabcellsep loan\tabcellsep before loan\tabcellsep after loan\tabcellsep loan\\
Income before loan\tabcellsep 1.000\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Income after loan\tabcellsep 0.864\tabcellsep 1.000\tabcellsep \tabcellsep \tabcellsep \\
Expenditure before loan\tabcellsep 0.827\tabcellsep 0.734\tabcellsep 1.000\tabcellsep \tabcellsep \\
Expenditure after loan\tabcellsep 0.767\tabcellsep 0.812\tabcellsep 0.857\tabcellsep 1.000\tabcellsep \\
Current loan\tabcellsep 0.113\tabcellsep 0.220\tabcellsep 0.092\tabcellsep 0.107\tabcellsep 1.000\end{longtable} \par
 
\caption{\label{tab_6}table :}\end{figure}
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