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\title{Impact of Microcredit Program of BRAC on Poverty Alleviation: A Case Study of Jhenaidah District in Bangladesh}
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             \author[1]{Md. Arif  Billah}

             \affil[1]{  International Islamic University Chittagong}

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\date{\small \em Received: 9 December 2019 Accepted: 3 January 2020 Published: 15 January 2020}

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\begin{abstract}
        


This main purpose of this study is to examine the role of conventional microcredit for poverty alleviation. Bangladesh Government and Non-Government Organizations (NGOs) have been trying to alleviate poverty of its people. NGOs provide microcredit to poor people to enhance their income and improve the living levels. The study reveals that microcredit programs have generated positive results for large numbers of the poor. Micro-credit has significant impacts on income, savings and expenditure levels of the household. The poor are not homogeneous, so impact varies significantly among different segments of the population according to their socio-economic status, gender, background, family composition and others. The analysis shows that in general, the microcredit is an instrument that can help to accumulate savings and income and finally assist to alleviate poverty. But still it is not only the instrument that can alleviate poverty but it is the vital component that can help to the poor people for standard living levels. Our analysis shows that after taking loan there is a significant change in the respondents? income savings and expenditures.

\end{abstract}


\keywords{poverty, microcredit, poverty alleviation.}

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\let\tabcellsep& 	 	 		 
\section[{Introduction a) Poverty}]{Introduction a) Poverty}\par
overty is a multi-dimensional phenomenon related to the inadequacy or lack of social, economic, cultural, and political entitlements \hyperref[b2]{(Momoh 2005}). Poverty is a multidimensional phenomenon that has different meaning for different people. Poverty can be viewed as absolute or relative, as a lack of income or failure to attain capabilities. It can be chronic or temporary, is sometimes closely associated with inequality and is often correlated with vulnerabilities, underdevelopment and economic exclusion Poverty is as an inability to afford the minimum standard of living. According to UNDP (2006), a person living under one dollar per day is considered to be living under the poverty line. In economic terms a country, region or household are poor when the per capita income of a country or the income of a household is very low. In political terms a country a region or a group of people are poor which are dependent on more powerful groups or individuals in order to express their own rights or choices. Poverty is hunger. Poverty is lack of shelter. Poverty is being sick and not being able to see a doctor.\par
Poverty is not having access to school and not knowing how to read. Poverty is not having a job, is fear for the future, living one day at a time. Poverty is losing a child to illness brought about by unclean water. Poverty is powerlessness, lack of representation and freedom (World \hyperref[b3]{Bank -2000)}. 
\section[{b) Microcredit}]{b) Microcredit}\par
Microcredit programs extend small loans to the very poor people for self employment projects that generate income, allowing them to care for themselves and their families. Microcredit also called "microfinance" and "micro lending" means providing small working capital loans to the self-employed people. Even small amounts of capital can make the difference between absolute poverty and a thriving little business generating enough income to feed the family, send kids to school, and build decent housing. Micro-credit refers to programs that are poverty focused and that provide financial and business services to the very poor people for generation of self-employment and income. Credit is a powerful instrument to fight against poverty. The role of micro-credit in reducing poverty is now well recognized all over the world. Governments, donors, development agencies, banks, universities, consultants, philanthropists and others have increasing interest in it. The universal objective of microfinance is to make it possible for large numbers of low-income people to access institutional financial services, hence the potential benefits of microfinance has accounted for its widespread adoption as an economic development, job creation and poverty reduction strategy. There is an ongoing debate whether credit alone or credit plus is needed for poverty reduction. There are views that credit alone on its own is inadequate to fight against poverty. The need for other services is also important in this respect. Such views, although, do not negate the role of credit; fail to appreciate the role of credit on its own merit. 
\section[{II.}]{II.} 
\section[{Objectives of the Study}]{Objectives of the Study}\par
The basic objective of this study is to analyze the impact of microcredit on household savings. For that the specific objectives are: ? To assess the impact of microcredit programs on poverty alleviation.\par
? To identify the socio-economic and demographic factors that affect household poverty status. ? To evaluate whether the microcredit programs are effective in accumulating savings of the participating households.\par
III. 
\section[{Research Methodology a) Data Collection and Sampling Techniques}]{Research Methodology a) Data Collection and Sampling Techniques}\par
The present study is based on primary data that have been collected by the researcher from Jhenaidah district of Bangladesh through questionnaire during January 2019 to June 2019 for the purpose of our analysis. A well-organized questionnaire is prepared for data collection. Our study draws the sample data and information from BRAC. The survey has been conducted with great care which covers different socio-economic information of 400 microcredit borrower. The selection of the respondents has been made using the multistage simple random sampling procedure. At the first stage, two out of six upazilas under Jhenaidah district are selected using simple random sampling method. The upazilas are Sailokupa and Kaligonj. There are 6 and 11unions under Sailokupa and Kaligonj upazilas, i\par
The sample size of the respondents was determined by using the formula (Guilford and Fruchter, 1978): respectively. At the second stage, using random sampling technique, from each upazila two unions are chosen which are Nittanondopur and Abaipur of Sailokupa upazila and Triechandpur and Rakhalgachi of Kaligonj upazila. At the third stage, two villages under each union are selected using random sampling technique. Finally, lists of respondents of the selected villages are collected from the upazila BRAC office. When adjusted for a non-responders rate of 10\%, N is 418. Hence, a sample of 418 respondents separately from the above mentioned financial institution was selected. Therefore 418 questionnaires were prepared and distributed randomly to the respondents, of which 400 filled-in questionnaires were returned. 
\section[{b) Techniques of Data Processing and Analysis}]{b) Techniques of Data Processing and Analysis}\par
The basic objective of this study is to analyze the impact of microcredit on savings of respondents after taking loan from BRAC that I have mentioned in the above points.\par
To analyze this, I have adapted the multiple linear regression models for regression analysis and I have analyzed independent samples test to compare respondent's savings level before and after taking loan. On the other hand, I have also analyzed frequency distribution of the respondents according to age, education, gender, number and earners in the household, occupation, size and residential status was presented in Tables and discussed in the paper. Computer software mainly SPSS version 20 are applied to analyze the data. Corrected standard errors are used to allow for heteroscedasticity. 
\section[{Model specification:}]{Model specification:}\par
Thus the multiple linear regression models were used and written as follows:?????????????? (??) = ?? + ?? 1 (????) + ?? 2 (??????) + ?? 3 (??????) + ?? 4 (??????) + ?? 5 (????) + ?? 6 (??????????) + ?? 7 (????) + ?? 8 (????) + ?? 9 (??????) + ?????????. i\par
Where the independent variables are: ? AR = 
\section[{c) Reliability and Validity Test}]{c) Reliability and Validity Test}\par
The reliability value of our surveyed data was 0.835 for variables. If we compare our reliability value with the standard value of 0.6 as recommended by Bagozzi and Yi's (1988), we find that the scale used by us are sufficient reliable for data analysis.\par
IV. 
\section[{Analysis of Findings a) Sample Characteristics}]{Analysis of Findings a) Sample Characteristics}\par
Table-1,2,3,4,5,6,7 summarizes the respondent's socio-economic and demographic characteristics according to their age, gender, and educational background, number of earners and dependents of households, occupation, residential status and family size. A number of researches in the region have found that those factors had significant impact on the respondents borrowing and standard living levels. The respondents' profile is discussed in the following sub sections. Table \hyperref[tab_1]{2} shows the gender distribution of the respondents. Field survey indicates that most of the microcredit borrowers are female (75.8\%) and male borrower is 24.3\%. Overwhelming majority of the respondents is married. Table \hyperref[tab_2]{3} shows that the married respondents are 83.5\%. It is assumed the marital status of microcredit borrower is the quite a reflection of our societal and cultural ideology and values.   Table \hyperref[tab_5]{5} shows that most of the respondents are educated and among them 33.0 \% are completed secondary school certificate (SSC) where 5.5 \% are graduate and 13.8\% are capable to sign only. Table \hyperref[tab_7]{7} shows that most of the households' earners are single which 78.8\% are and rests of them are dependents. In indicates a large number of people are not self employed or employed.\par
V. Impact of Conventional Microcredit on Respondent's Savings (After Loan)\par
In this study we have estimated simultaneous equations model to determine the impact of microcredit on respondent's savings level. Total savings of the respondent are estimated as the dependent variables. Age of the respondent (AR), Education of respondent (ER), Gender of the respondent (GR), Number of earners in the household (EAR), Number of dependents in the household (DHR), the amount of loan (AL), expenditure for food and non-food items (per month) after loan, Earners in the Household, Occupation, Gender of Respondent, Size of the household (SH), Interest rate (IR) are estimated as independent variables. The value of the coefficient AR (Age of the Respondent) is negative sign, it implies that savings decreases due to youth unemployment and inexperience. This implies that, with an increase in the age of the respondent the probability being poor will fall as a result the savings of the respondent increases. In the context of our country, there is a joint family system in rural areas, the older of the respondent, the higher of the respondent's earnings and accumulation of resources. Field survey data shows that the incidence of poverty increases with youth unemployment and inexperience and incidence of poverty decreases with age of the respondent. 
\section[{a) Regression results of savings (s) and independent variables}]{a) Regression results of savings (s) and independent variables}
\begin{quote}
Model\end{quote}
\par
The value of the coefficient SH (Size of the Household) is negative sign. This implies that, the household size has a negative impact on the household income. The higher the household size the lower the household income. With a decrease in the household size the probability being poor will fall, as a result the income of the family increases. This conclusion is consistent with the earlier observation that the incidence of poverty increases with the household size. The coefficient of household size is -.004, it means that if other things are remaining the same, the size of household increases by one unit which will reduce the household income by 0.4. Field survey data shows that the incidence of poverty increases with the increases of household size.\par
The coefficient of ER (Education of the Respondent) has a negative effect on poverty and income. This implies that the more educated an individual, the greater the potential to exploit resources and technology and avoid poverty, higher the education higher the savings of the family. The coefficient of household education is 0.026; it means that if other things are remaining the same, one per cent increases in education of household will increase the household savings by 2.6. Field survey data shows that the incidence of savings decreases with the increases in education of household.\par
The coefficient of EAR (Earners in the household) has negative effects on poverty. This implies that the more earners of the household lower the poverty and increase the household welfare. The coefficient of earner of household is .009; it means that if other things are remaining the same, one unit increases in earners of household will increase the household savings by 0.9. Field survey data shows that the incidence of poverty decreases with the increases in earners of household.\par
The coefficient of DHR (Number of dependents in the household) has positive effects on poverty, higher the dependency ratio lowers the income of the family. The coefficient of dependency ratio is -0.010, it means that if other things are remaining the same, one unit increases in dependency ratio of household will decrease the household savings by 1.0. Field survey data shows that the incidence of poverty increases with the increases of dependency ratio.\par
Total income of the respondent (TYR) plays a vital role to accumulate savings that can tend to raise investment for the development of rural Bangladesh. From the field survey, it is evident that the coefficient of TYR (Total Income of the Respondent) has negative impact on poverty, increases of TYR, and increases of savings of the respondent. The coefficient of respondent's savings is .117; it means that if other things are remaining the same, one unit increases in savings of respondent will increase the respondent's asset by 1.7. Field survey data shows that the incidence of poverty decreases with the increases of savings of respondent.\par
There are more occupations create in rural Bangladesh. From the field survey, it is evident that the coefficient of occupation of the respondent (OR) has positive impact on poverty alleviation. The coefficient of respondent's occupation is 0.059; it means that if other things are remaining the same, occupational status does ensure the variation on savings of the respondent.\par
The coefficient of interest rate (IR) has negative effects on income and savings, higher the interest rate lowers the income of the family. The of interest rate is -0.20, it means that if other things are remaining the same, one unit increases in interest rate on the amount of loan will decrease the respondent's income by 2.0. Field survey data shows that the incidence of poverty increases with the increases of interest rate.\par
Total expenditures for food and non-food items of the respondent (TExpR) play a vital role to measure living levels of households. From the field survey, it is evident that the coefficients of expenditure for food and non-food items are -0.963 and -0.807. It means that if other things are remaining the same, one unit decrease in expenditure of respondent will increase the respondent's savings by 6.3 and 0.7. Field survey data shows that the incidence of poverty decreases with the increases of savings of respondent. Table \hyperref[tab_9]{8} shows comparative savings statistics of respondent (before and after loan). After loan around 271 numbers of respondents say that microcredit can accumulate savings and it is an instrument that can help us to alleviate poverty and self-reliability. 
\section[{VI.}]{VI.} 
\section[{Major Findings}]{Major Findings}\par
From the estimation it may be concluded that the microcredit programs are effective in generating higher income and savings for borrowers. This is a very important finding as it confirms the claims made by the microcredit providers. We may conclude from this finding that microcredit is providing better quality of life for its borrowers. We know from experience that people's income increases as they grow older. Our estimation results suggest that age of the respondent has a significant and positive impact on income and assets. As someone grows older them income also grows. Our results also suggest that educations of the respondents are an important factor in affecting income and assets positively. Education helps people to be more enlightened. According to this finding, education enhances borrowers' income and assets. We have also found from this analysis that infrastructural facilities in the village facilitates borrowers' income and assets. This study also suggests that as the number of earners increases in a household, amount of borrowing also increases. This could be due to the fact that having more earning members increases the ability to repay loans quickly as well as obtain more loans. Infrastructural facilities facilitate the household outcomes. It is also found that families having more earning members enjoy better household outcomes. 
\section[{VII. Challenges in Microcredit Programs a) Cycle of loans and liability}]{VII. Challenges in Microcredit Programs a) Cycle of loans and liability}\par
Microcredit has changed life style of the poor people but they could not come out from the causes of poverty. Sometimes they use microcredit loan from one organization to meet interest obligations from another. It chained the poor people, so they are rolling in the cycle of loans and liability. 
\section[{b) Highly expensive}]{b) Highly expensive}\par
Microcredit imposes high interest on the borrowers. So, many borrowers fail to repay the loans. As a result they are carrying the burden of high interest which is not at all in favor of eradicating poverty. 
\section[{c) The human system for collecting installment}]{c) The human system for collecting installment}\par
The way micro credit lenders collect the installment of the loans is really pitiable. Most of the field officers are in a position of power locally and are judged on repayment rates as the primary metric of their success. They sometimes use force and even violent tactics to collect installments on the microcredit loans. Many borrowers bound to sale there their last belongings to pay the installment. Many committed suicide under frustration. 
\section[{d) Increases dowries}]{d) Increases dowries}\par
A large number of people believe that, microcredit increase the dowries. Poor parents take loan for their daughter to meet the demand of the grooms. Many people put pressure on women to borrow from the micro financer. Micro credit is indirectly responsible for increasing dowries. Ignorance to agriculture sector: Micro credit could not play any significant role in the agriculture sector. But it is simply impossible eradicate poverty from our country without developing this sector. 
\section[{VIII. Recommendations and Conclusion}]{VIII. Recommendations and Conclusion}\par
Microcredit, originated in Bangladesh has blown out all over the globe. Today within the international coverage of microcredit Bangladesh's achievement stands out prominently. The world community has appreciated the contributions of Bangladesh in the field of microcredit, which was evident in the Asia Pacific Region Microcredit Summit (APRMS) Meeting of Councils in Dhaka, Bangladesh from 16-19 th February, 2004 arranged by Palli Karma -Sahayak Foundation (PKSF), in association with the Microcredit Summit Campaign based in Washington D.C. More than 1200 participants from 47 countries from different parts of the globe pledged their firm commitment to take forward the microcredit movement. Bangladesh government has also placed adequate emphasis on microcredit programs. The Interim Poverty Reduction Strategy Paper (IPRSP) of the government of Bangladesh has outlined some important roles for microcredit. The policy makers have recognized the importance of microcredit in Bangladesh and the present government has pledged its support to this program.\par
To become micro credit programs more fruitful we should give emphasis on the followings -? Ensuring close monitoring.\par
? Using micro credit in high profitable business.\par
? Fixing reasonable rate of interest. ? Making sufficient laws for the operation of micro credit.\par
? Ensure suitable behavior of by the officers.\par
? Give more emphasis on rural economic productive sector, especially in agriculture.\par
? Ensure more research in micro credit and its effectiveness. If we fulfill these requirements, we could hope that micro credit becomes a tool of eradicating poverty. Only then we can get poverty and hunger free Bangladesh.  \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.019318181818181818\textwidth}P{0.23954545454545453\textwidth}P{0.23954545454545453\textwidth}P{0.07727272727272727\textwidth}P{0.20863636363636362\textwidth}P{0.06568181818181817\textwidth}}
\tabcellsep Age in Years\tabcellsep \multicolumn{4}{l}{Frequency Percent Valid Percent Cumulative Percent}\\
\tabcellsep 20 thru 30\tabcellsep 264\tabcellsep 66.0\tabcellsep 66.0\tabcellsep 66.0\\
\tabcellsep 31 thru 40\tabcellsep 69\tabcellsep 17.3\tabcellsep 17.3\tabcellsep 83.3\\
Valid\tabcellsep 41 thru 50\tabcellsep 44\tabcellsep 11.0\tabcellsep 11.0\tabcellsep 94.3\\
\tabcellsep 51 thru highest\tabcellsep 23\tabcellsep 5.8\tabcellsep 5.8\tabcellsep 100.0\\
\tabcellsep Total\tabcellsep 400\tabcellsep 100.0\tabcellsep 100.0\tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{Source: Author's Field-work (2018)}\end{longtable} \par
 
\caption{\label{tab_0}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{P{0.07923728813559322\textwidth}P{0.10805084745762711\textwidth}P{0.2809322033898305\textwidth}P{0.09364406779661016\textwidth}P{0.09364406779661016\textwidth}P{0.1944915254237288\textwidth}}
\multicolumn{2}{l}{Gender}\tabcellsep \multicolumn{3}{l}{Frequency Percent Valid Percent}\tabcellsep Cumulative Percent\\
\tabcellsep Male\tabcellsep 97\tabcellsep 24.3\tabcellsep 24.3\tabcellsep 24.3\\
Valid\tabcellsep Female\tabcellsep 303\tabcellsep 75.8\tabcellsep 75.8\tabcellsep 100.0\\
\tabcellsep Total\tabcellsep 400\tabcellsep 100.0\tabcellsep 100.0\tabcellsep \end{longtable} \par
  {\small\itshape [Note: Source: Author'sField-work (2018)    ]} 
\caption{\label{tab_1}Table 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{P{0.08682795698924732\textwidth}P{0.12338709677419356\textwidth}P{0.27876344086021504\textwidth}P{0.07311827956989247\textwidth}P{0.22849462365591397\textwidth}P{0.05940860215053763\textwidth}}
\multicolumn{2}{l}{Marital Status}\tabcellsep \multicolumn{4}{l}{Frequency Percent Valid Percent Cumulative Percent}\\
\tabcellsep Single\tabcellsep 34\tabcellsep 8.5\tabcellsep 8.5\tabcellsep 8.5\\
Valid\tabcellsep Married Divorced\tabcellsep 334 32\tabcellsep 83.5 8.0\tabcellsep 83.5 8.0\tabcellsep 92.0 100.0\\
\tabcellsep Total\tabcellsep 400\tabcellsep 100.0\tabcellsep 100.0\tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{Source: Author's Field-work (2018)}\end{longtable} \par
 
\caption{\label{tab_2}Table 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.10182291666666667\textwidth}P{0.11510416666666666\textwidth}P{0.27447916666666666\textwidth}P{0.07526041666666666\textwidth}P{0.22578125\textwidth}P{0.05755208333333333\textwidth}}
\multicolumn{2}{l}{Residential Status}\tabcellsep \multicolumn{4}{l}{Frequency Percent Valid Percent Cumulative Percent}\\
\tabcellsep Urban\tabcellsep 28\tabcellsep 7.0\tabcellsep 7.0\tabcellsep 7.0\\
Valid\tabcellsep Semi Urban Rural\tabcellsep 200 172\tabcellsep 50.0 43.0\tabcellsep 50.0 43.0\tabcellsep 57.0 100.0\\
\tabcellsep Total\tabcellsep 400\tabcellsep 100.0\tabcellsep 100.0\tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{Source: Author's Field-work (2018)}\end{longtable} \par
 
\caption{\label{tab_3}Table 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{}
\end{longtable} \par
 
\caption{\label{tab_4}Table 4}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{5} \par 
\begin{longtable}{P{0.07819999999999999\textwidth}P{0.187\textwidth}P{0.22440000000000002\textwidth}P{0.085\textwidth}P{0.20059999999999997\textwidth}P{0.07479999999999999\textwidth}}
\multicolumn{2}{l}{Educational Status}\tabcellsep \multicolumn{4}{l}{Frequency Percent Valid Percent Cumulative Percent}\\
\tabcellsep Sign Only\tabcellsep 55\tabcellsep 13.8\tabcellsep 13.8\tabcellsep 13.8\\
\tabcellsep Primary\tabcellsep 124\tabcellsep 31.0\tabcellsep 31.0\tabcellsep 44.8\\
Valid\tabcellsep Secondary Higher Secondary\tabcellsep 132 67\tabcellsep 33.0 16.8\tabcellsep 33.0 16.8\tabcellsep 77.8 94.5\\
\tabcellsep Graduate\tabcellsep 22\tabcellsep 5.5\tabcellsep 5.5\tabcellsep 100.0\\
\tabcellsep Total\tabcellsep 400\tabcellsep 100.0\tabcellsep 100.0\tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{Source: Author's Field-work (2018)}\end{longtable} \par
 
\caption{\label{tab_5}Table 5 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{6} \par 
\begin{longtable}{P{0.016732283464566927\textwidth}P{0.23090551181102362\textwidth}P{0.11043307086614172\textwidth}P{0.0937007874015748\textwidth}P{0.31456692913385825\textwidth}P{0.08366141732283465\textwidth}}
\tabcellsep Occupation\tabcellsep \multicolumn{2}{l}{Frequency Percent}\tabcellsep \multicolumn{2}{l}{Valid Percent Cumulative Percent}\\
\tabcellsep Day Labor\tabcellsep 63\tabcellsep 15.8\tabcellsep 15.8\tabcellsep 15.8\\
\tabcellsep Farmer\tabcellsep 77\tabcellsep 19.3\tabcellsep 19.3\tabcellsep 35.0\\
\tabcellsep Small Business\tabcellsep 97\tabcellsep 24.3\tabcellsep 24.3\tabcellsep 59.3\\
Valid\tabcellsep Shopkeeper\tabcellsep 101\tabcellsep 25.3\tabcellsep 25.3\tabcellsep 84.5\\
\tabcellsep Housewife\tabcellsep 48\tabcellsep 12.0\tabcellsep 12.0\tabcellsep 96.5\\
\tabcellsep Others\tabcellsep 14\tabcellsep 3.5\tabcellsep 3.5\tabcellsep 100.0\\
\tabcellsep Total\tabcellsep 400\tabcellsep 100.0\tabcellsep 100.0\tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{Source: Author's Field-work (2018)}\end{longtable} \par
 
\caption{\label{tab_6}Table 6 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{7} \par 
\begin{longtable}{P{0.12973684210526315\textwidth}P{0.08052631578947368\textwidth}P{0.27289473684210525\textwidth}P{0.07605263157894737\textwidth}P{0.2281578947368421\textwidth}P{0.06263157894736841\textwidth}}
\multicolumn{2}{l}{Earners in the Household}\tabcellsep \multicolumn{4}{l}{Frequency Percent Valid Percent Cumulative Percent}\\
\tabcellsep 1.00\tabcellsep 315\tabcellsep 78.8\tabcellsep 78.8\tabcellsep 78.8\\
Valid\tabcellsep 2.00 3.00\tabcellsep 74 11\tabcellsep 18.5 2.8\tabcellsep 18.5 2.8\tabcellsep 97.3 100.0\\
\tabcellsep Total\tabcellsep 400\tabcellsep 100.0\tabcellsep 100.0\tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{Source: Author's Field-work (2018)}\end{longtable} \par
 
\caption{\label{tab_7}Table 7 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{8} \par 
\begin{longtable}{P{0.02056451612903226\textwidth}P{0.15766129032258064\textwidth}P{0.07711693548387097\textwidth}P{0.02056451612903226\textwidth}P{0.06683467741935484\textwidth}P{0.044556451612903225\textwidth}P{0.09596774193548387\textwidth}P{0.06683467741935484\textwidth}P{0.1216733870967742\textwidth}P{0.13366935483870968\textwidth}P{0.044556451612903225\textwidth}}
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{4}{l}{Independent Samples Test}\tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep \multicolumn{2}{l}{Levene's Test}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep \multicolumn{2}{l}{for Equality of}\tabcellsep \tabcellsep \tabcellsep \multicolumn{3}{l}{t-test for Equality of Means}\tabcellsep \\
\tabcellsep \tabcellsep \multicolumn{2}{l}{Variances}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep F\tabcellsep Sig.\tabcellsep t\tabcellsep df\tabcellsep Sig. (2-t ailed)\tabcellsep Mean Difference\tabcellsep Std. Error Difference\tabcellsep \multicolumn{2}{l}{95\% Confidence Interval of the Difference Lower Upper}\\
\tabcellsep Equal\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep variances\tabcellsep .11\tabcellsep .074\tabcellsep 4.78\tabcellsep 398.00\tabcellsep .00\tabcellsep 960.05\tabcellsep .074\tabcellsep 565.41\tabcellsep 1354.70\\
\tabcellsep assumed\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
TSR AL\tabcellsep Equal\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep variances not\tabcellsep \tabcellsep \tabcellsep 4.84\tabcellsep 260.15\tabcellsep .00\tabcellsep 960.05\tabcellsep .019\tabcellsep 569.78\tabcellsep 1350.32\\
\tabcellsep assumed\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep Equal\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep variances\tabcellsep 1.23\tabcellsep .027\tabcellsep .76\tabcellsep 398.00\tabcellsep .00\tabcellsep 113.48\tabcellsep .074\tabcellsep 178.94\tabcellsep 405.89\\
\tabcellsep assumed\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
TSR BL\tabcellsep Equal\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep variances not\tabcellsep \tabcellsep \tabcellsep .69\tabcellsep 196.68\tabcellsep .00\tabcellsep 113.48\tabcellsep .027\tabcellsep 212.46\tabcellsep 439.41\\
\tabcellsep assumed\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{3}{l}{Source: Author's Field-work (2018)}\end{longtable} \par
 
\caption{\label{tab_9}Table 8 :}\end{figure}
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\end{document}
