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\title{Determinant Factors Affecting Loan Repayment Performance of Women Borrowers from Micro Finance Institutions in Southwest Ethiopia: Evidence from Four Woredas around Gilgel Gibe Hydroelectric Power Dam}
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             \author[1]{Fikadu  Gutu}

             \affil[1]{  JIMMA UNIVERSITY}

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\date{\small \em Received: 8 December 2016 Accepted: 4 January 2017 Published: 15 January 2017}

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\begin{abstract}
        


The aim of microcredit is to help the poor and lower income group to get funds for their business activities and to improve their lives. Also Ethiopian government use and promote microcredit as a bridge to eradicate poverty and for women's empowerment. In reverse, many women?s are loan defaulter than other borrowers. But, there are a limited number of researches attempted to explore determinant factors affecting loan repayment performance in the case of women borrower of micro finance institutions by using empirical data, especially in the study country and almost no for the study area. So to fill this gap, we conduct a cross-sectional study with overall aim of assessing determinant factors affecting loan repayment performance in the case of women borrowers from micro finance institutions. We employed a multi-stage sampling approach and a total of 182 women?s, which 85 borrowers were defaulter and 97 non-defaulters.

\end{abstract}


\keywords{loan repayment, women borrowers, MFIs, binary logistic regression.}

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\section[{Introduction a) Background of the Study}]{Introduction a) Background of the Study}\par
omen constitute half of the world's population, accomplish nearly two thirds of its work hours but still receive only one tenth of income and one percent of world's property. Also gender discrimination is dissimilar and common in developing countries \hyperref[b8]{(Kalai, 2004)}.\par
Microfinance Institutions, financial services designed for poor people has been renowned for its ability to reach out to women and enhance their welfare. Study conducted in Kenya shows that, Women entrepreneurs in Kenya are the key to economic growth because they are generating employment. However, women owned businesses could contribute more than what they are doing today. A growing amount of research shows that countries that fail to address gender barriers are losing out on significant economic growth. Without increased attention to the gender dimensions of economic development, Kenya is therefore unlikely to meet its growth targets. This leads to demonstrates that addressing gender barriers in Kenya could generate significant economic growth for the country \hyperref[b15]{(Omonywa and Muturi, 2015)}. \hyperref[b1]{Goetz and Gupta (1996)} and Noponen (1990) argued that, impact on empowerment cannot be inferred from take-up of financial services or repayment levels. Women may repay through taking loans elsewhere and getting into serious debt while loans may be controlled by men. Some researchers have expressed concerns that women's micro-finance programs may be merely using women as unpaid debt collectors mediating between development agencies and male family members, increasing their dependency on men and/or conflicts between women to fulfill repayment targets.\par
According to \hyperref[b7]{Kabeer (2001)}, "?these conflicting conclusions about the 'empowerment' potential of credit for women are both apparent and real. What appears to be contradictory findings concerning, for instance, the extent to which credit exacerbates or lessens violence against women, enables or fails to enable them to acquire independent assets, is associated with an increase or decrease in their living standards is partly a difference in methodology. It reflects the fact that some studies relied largely on statistical data and significance tests for their findings while others relied on more qualitative, sometimes anecdotal, evidence. Conflicting conclusions about the impact of credit also reflect differences in the questions asked by different evaluations. "In conclusion she argued that, by and large, the negative evaluations focused on processes of loan use while the positive ones focused on outcomes associated with, and attributed to, access to loans. The validity of both sets of measures depends on their conceptual clarity and on the validity of their underlying grounds.\par
Undeniably women entrepreneurs around the world are making a difference. They contribute numerous ideas and a great deal of energy and capital resources to their communities, and generate jobs as well as create additional work for suppliers and other spin-off business linkages (Common wealth secretariat, 2002). Siwadi and Mhangami, 2011 adds that, it is undeniable that women entrepreneurs are the major actors in that sector and contributors to economic development and are becoming increasingly visible in the local economies of the developing counties.\par
Generally, the main objective of the study was to identifying determinant Factors affecting Loan Repayment performance of women borrowers from Micro Finance Institutions in South West Ethiopia, in the Case of four woredas around Gilgel Gibe Hydroelectric Power Dam. 
\section[{b) Justification of the Study}]{b) Justification of the Study}\par
According to many studies around the world Women entrepreneurs have been seen as a major force for innovation, job creation and economic growth. Even though the crucial role they play, women entrepreneurs continue to face a number of challenges. For example: loan repayment default (failure to repay their loans in time), discrimination (at household and organizational level), lack of access to finance, lack of education, and lack of training regarding to their business are few amongst to others. Though there is growing interest in women funding through women enterprise fund and a number of micro-financing institutions. Kamanza, (2014) state that, loan repayment by women is a big challenge which leads to reduction of their potential to positively contribute to the economy.\par
One way to tackle the loan repayment problem is to investigate the factors which affect the loan repayment of MFIs., although loan repayment is determined by willingness, ability and other characteristics of the borrowers; businesses characteristics and characteristics of the lending institutions including product designs, training, credit rationing and suitability of their products to borrowers. Regarding their characteristics, even if it is the only case; repayment of loans depends on the willingness and ability of the borrowers to repay was the main. Therefore, individual borrowers can either repay their loans or choose to default. It is also true that the factors influencing loan repayment capacity among borrowers are not only likely to differ by programs but also differ from country to country depending on the domestic business and economic environment (Tundui, C. and Tundui, W., 2013).\par
Study conducted by Fikadu G. and Wondaferahu M., (2016) stated that, even if micro finance institutions have a positive impact/role on women economic empowerment, loan repayment problem was seen on some women clients of micro finance institutions. Therefore, the researchers were motivated to look into the determinant factors affecting loan repayment performance of women borrowers of micro finance institution clients in the study area. 
\section[{II.}]{II.} 
\section[{Literature Review a) Microcredit and Loan Repayment}]{Literature Review a) Microcredit and Loan Repayment}\par
Microcredit is just a small credit given to the poor that engaged in microenterprise or for the purpose of income-generating activities. On the other hand, microfinance encompasses broad financial services given to the poor and low-income group for many reasons and not just for income generating activities. Woller\& Parsons (2002) describe microfinance as the second revolution in credit theory and policy where the first revolution is microcredit. Microfinance institutions (MFIs) were established to fill the gap in the financial services sector by providing funds to the poor and lower income group and thus alleviating poverty and enhance their business activities. The Microfinance Institutions provide funds for start-up business or for working capital.\par
In addition, some Microfinance Institutions also provide funds for non-business activities such as for education and emergencies purpose. In the credit market, agency problem, moral hazard and adverse selection exist because of information asymmetries. Information asymmetries are the main obstacle for Microfinance Institutions to provide loans to clients. Financial institutions usually requires business proposal, borrower past credit information and collateral before approving the loan. MFIs offer credit through groupbased lending method to mitigate agency problems, moral hazard and adverse selection and to replace the collateral requirement. In group-based lending, borrowers must form a group before applying loans and they also responsible to other loan members. If one member default, the others will be responsible to pay the loan or they will be denied access for the next loans.\par
Microfinance Institutions are usually nongovernmental organizations (NGOs) who are not profitoriented. NGOs assume poverty is created through social processes that deprive the poor of their rightful access to social resources, including credit. These NGOs help the poor to find credits to support their small enterprises or income-generating activities. These institutions acted as a financial intermediation like formal bank. The difference between formal banks and microfinance institutions (MFIs) is the former focus on rich clients, while the latter to MFIs clients who are poor people. According to \hyperref[b16]{Remenyi (2000)}, subsidized credit and subsidized banking with the poor are inimical to "best practice in microfinance". Moreover, Microfinance Institutions also offered skills training and marketing to their clients. 
\section[{b) Microcredit and Women Borrowers}]{b) Microcredit and Women Borrowers}\par
Beverly et al., 2011 states that, the government and the corporate world have come up with a number of financing schemes aimed at providing loans to women entrepreneurs. According to Kamanza 2014, this effort mostly turns to be unfruitful due to poor loan repayment. In addition, business failure influences loan, gender roles, borrower's entrepreneurial skills and diversion of loan funds by borrowers. These factors have made the financial institutions to be skeptical about the entrepreneurial abilities of women.\par
Lending to the poor or lower income group raises many debates among practitioners and academicians. The poor are usually excluded from credit facilities because of many reasons. These include insufficient collateral to support their loans, high transaction costs, unstable income, lower literacy and high monitoring costs. Usually they survive through involvement in micro business activities or informal activities that comprises food processing and sales, small scale agriculture, services, crafts and petty trading. However, these activities actually contribute a number of total employment and gross domestic product (GDP) to the country. Micro and small enterprises (MEs) have been recognized as a major source of employment and income in many countries of the low income country \hyperref[b14]{(Mead \&Liedholm, 1998)}. 
\section[{c) Microfinance Program and Women's Participation in Ethiopia}]{c) Microfinance Program and Women's Participation in Ethiopia}\par
According to \hyperref[b4]{Itana et al, 2004}; until 1990s, the sources of finance for rural and urban poor and micro and small enterprise operators in Ethiopia were confined only to informal sources of finance like Arataabedar (local money lenders in local version), and relatives are the main sources. They also added that, starting in the mid-1990s, following the drought of 1984/85, some Non-Government Organizations (NGOs) introduced the idea of saving and credit among poor people as a strategy for rehabilitation and development, which the local people assumed as aid. But when government programs operated with in collaboration of international financial institutions came into the picture.\par
With the substantial measures taken to liberalize the financial sector, Ethiopian government take action for emphasizing the role of MFIs by made proclamation. Micro-financing is taken as a core to eradicate poverty through implying capital to subsistence agriculture and micro enterprises. Following the Agricultural Development Led Industrialization strategy of the Ethiopian government, financing rural community has been considered as an important tool for agricultural and food security \hyperref[b0]{(Belay, 2002)}.\par
In order with the development of micro-finance institutions, the Government of Ethiopia arrangement participatory rules and policies which gave room for women productivity. Padma and Swamy (2003) noted that, government has formulated and issued the Ethiopian Women's Policy to accelerate the economic and social improvement of women. This new policy gives special attention to rural women by 'facilitating the necessary conditions whereby they can have access to basic services and to ways and means of lightening their workload'. Consequently, all development programs at national and regional levels should be able to integrate gender concerns in their plans and programs to guarantee that women participate, contribute, benefit recognized. 
\section[{III.}]{III.} 
\section[{Methodology of the Study a) Study Area and Design}]{Methodology of the Study a) Study Area and Design}\par
A cross-sectional Study design was employed to look Factors affecting Loan Repayment of Micro Finance Institutions borrowers in South West Ethiopia. Among the four woredas', two weredas, namely, Omo Nada and Sokoru was selected and from these two woredas', 6-pairs of villages are selected and paired based on various comparability factors, including similarity on, infrastructure availability, communication facilities and other socioeconomic characteristics, such as literacy rate, topography, access to electric power, and presence of other development programs. 
\section[{b) Sampling Method and Sample Size}]{b) Sampling Method and Sample Size}\par
For this study multi-stage probability sampling techniques were used. Since the members are large in number they are divided by groups and randomly selected for data collection. 182 samples have been collected for the research from all selected areas for the study. All women at working age and residing in the selected kebeles for more than six month consitituted as the study population. 
\section[{c) Method of Data Collection and Sources of Data}]{c) Method of Data Collection and Sources of Data}\par
A structured interview schedule questioner was prepared by the researchers and used for collecting data from the rural and sub-urban women who are member of Micro finance institution for more than one year. The study was undertaken in rural and sub-urban areas of the study region. Both primary and secondary data's were used. Primary data is enumerated from a field survey in the study area. Secondary data were collected from governmental and non-governmental organization reports, internet and related documents. 
\section[{d) Data Analysis and Estimation Techniques of Econometric Model}]{d) Data Analysis and Estimation Techniques of Econometric Model}\par
Data analysis was done after all the relevant data collection from the respondents. The empirical analyses of the study were conducted using both descriptive statistics and econometric regression model. Descriptive statistics discussion were made by using measures like percentages and cross-tabulation used for comparing borrowers not paid credit/defaulters/ and paid credit/ non defaulters /in various explanatory variables and to interpret the data.\par
To analyze studies that involves qualitative choice, especially to evaluate dichotomous variables most studies used logit and probit. The logit and probit formulations are quite comparable, the main difference being that the former has slightly flatter tails; that is, the normal curve approaches the axes more quickly  {\ref (Gujarati, 1988)}. Logit model has got advantage over probit in the analysis of dichotomous outcome variable in that, it is extremely flexible and easily used model from mathematical point of view and results in a meaningful interpretation. \hyperref[b2]{(Hosmer and Lemeshew, 1989)}.\par
Therefore, binary logistic regression model was applied for analyzing the qualitative data which deals with loan repayment performance on nine explanatory variables that would be included in the study. Loan repayment status was a dependent variable, while different background characteristics of respondent, socioeconomic, business related and micro finance institution related factors would be considered as independent variables. In this case the value of this dependent variable (loan repayment performance) is 0 and 1, if borrowers paid a loan on time it takes 1 and otherwise 0. The functional relationship between the probability of loan repayment performance and explanatory variables specified as: Let Y ij be the i th women's loan repayment performance (a binary outcome, 1= alone, 0=otherwise) living in the j th kebele.?? ???? \textasciitilde ??????????????????(?? ?? ) ?????? ?? ?? 1 ? ?? ?? = ?? 0 + ?? 1 ?? 1 + ?? 2 ?? 2 + ? + ?? ?? ?? ??\par
where, ?? ?? is the population proportion of loan repayment performance of women's in the j th kebele, ??  
\section[{Result and Discussion}]{Result and Discussion} 
\section[{a) Back Ground Characteristics of the Respondents}]{a) Back Ground Characteristics of the Respondents}\par
Information collected on the characteristics of the households and respondents was important to, understand and interpret the finding of the survey and provide indicators of the representativeness of the survey. The information is also useful in understanding and identifying the possible factors that affect loan repayment performance women borrowers.\par
Among 182 of households, 142(78\%) were male headed and 40(22\%) are female headed. The average household size, 139(76.3\%) were less than or equal to 5 membership and 43(23.6\%) are greater than 5 household member size. As expected from total respondents 133(73.1\%) no education or they are not write and read at all, 41(22.5\%) primary education and 8(4.4\%) secondary and higher education. Out of total population of study between age of 25-32 covers the highest percent which is 138(75.8\%) and the lowest percent of age group was between 40-47 years of age, which count 7(3.8\%). Lastly, 142(78\%) respondents are married or live with their husband, 29(15.9\%) divorced and only 11(6\%) of them are widowed (Annex I; Table \hyperref[tab_1]{1}) Generally result of the study survey indicates that, households in the study area were predominantly male headed, which shows almost the same result with the study country and also a common feature of most African countries. Almost around one in four households are headed by women with the proportion of femaleheaded households much higher in urban than in rural areas. Depending on the finding and theoretical arguments it is possible to say that, family sizes in rural areas were higher than urban area. 
\section[{b) Asset Ownership of Respondents and their Household}]{b) Asset Ownership of Respondents and their Household}\par
The study survey result shows that, 169(92.6\%) of household respondents have their own houses while 13(7.2) respondent's household does not have it. Meanwhile 158(86.9\%) of respondents indicated the floor of their house is natural floor, while 20(10.9\%) rudimentary floor and 4(2.2\%) indicated their houses having finished floor. Concerning the main construction material for their home, 99(44.4\%) of the respondents used natural roof, 3(1.6\%) rudimentary roof and 80(44\%) of respondents household finished roof. In addition 134(73.6\%) of respondent used natural wall, 43(25.8\%) rudimentary wall and only 1(0.5\%) have finished wall. However, only 59(32.4\%) of respondents indicated that they have electricity while the majorities 123(67.8\%) do not have electricity in their home (Annex I; Table \hyperref[tab_3]{2}).\par
Regarding to household effect, majority of respondents 169(94.5\%) of households have radio and only 9(5.5\%) of them does not have it. However, only 31(17\%) households have television at home. Among the total respondents of the study, around one third of them said that, their household does not have their own land for agricultural. This indicates that households, in rural Ethiopia are much less likely to possess consumer items like televisions, radios and electricity when compared with urban areas (Ibid). 
\section[{c) Determinant Factors affecting Loan Repayment Performance of Women Borrowers'}]{c) Determinant Factors affecting Loan Repayment Performance of Women Borrowers'}\par
The result of binary logistic regression model on determinant factors affecting loan repayment performance of borrowers is presented in table I below. A total of nine explanatory variables were considered in the econometric model. Out of which six variables were found to be significant. These were age of respondents, education level of respondents, Sufficiency loan to start business/intended purpose, residence type, using of loan for intended purpose, and number of group member. The coefficients of half of these significant variables were negative and half of them were positive, those are using of loan for intended purpose, number of group member and sufficiency of loan to start business or sufficiency of loan for intended purpose. However, the extent to which these variables relate with the dependent variable is different.\par
The age variable was negatively and significantly influencing loan repayment at 5\% significant level. If the other variables held constant, a unit increase in the respondents' age decreases the probability of being defaulter by 0.173 times when compared to the other category. It is possible to say that through a time aged respondents more responsible for their activities became settled and accumulate wealth more than youngsters.\par
The education level was negatively and significantly influencing loan repayment at 5\% significance level. The probability of the loan repayment rate of educated/secondary and above educational level respondents to became loan repayment defaulter is higher by 0.008 when compared to respondents those are no education/not write and read, other variables are remaining constant. This figure revels that the borrowers whose educational level increased by attending one more year at school have the probability of decreasing the loan repayment performance/becoming defaulter by 0.008 times less than the borrowers who have lesser education level/illiterates. This may be suggests that more educated borrowers are governmental workers and the inflation problem happened in the country may be have impact on loan repayment performance. This finding result was not in similar with the study conducted by Shaik Abdul M. P. and Tolosa N., at 2014. Sufficiency of Loan to Start Business variable was positively and significantly influencing borrowers' loan repayment performance. It became significant predictor of borrowers' loan repayment performance at 5\% significance level. As indicated under binary logistic regression result, Sufficiency loan to start business/for intended purpose increases the borrowers' loan repayment probability by 11.03 times. But this result was in parallel with study conducted by J.T.O.Oke, et al, (2007) and, Shaik Abdul M. P. and Tolosa N., 2014. Therefore, these positive preconditions enable borrowers to enhance loan repayment performance better.\par
Type of Residence variable also found to influence borrowers' loan repayment performance negatively and significantly at 5\% significance level. Keeping the other factors constant, living in rural area were decreases the probability of being defaulter by 0.015 times. This is may be inflation problem happened in the country before and at the time of data collection. According to country governmental as well as private media reports the inflation affects more urban areas community rather than rural area. Also Using of Loan for Intended Purpose variable was found to influence positively and significantly the borrowers' loan repayment performance at 5\% significance level. Using of loan for intended purpose increases the borrowers' loan repayment performance probability by 3.32 times. This study result was parallel with the study conducted by Shaik Abdul M. P. and Tolosa N., 2014.\par
The number of group member's variable was positively and significantly influencing borrowers' loan repayment performance. It became significant predictor of borrowers' loan repayment performance at 5\% significance level. As indicated under binary logistic regression result, when number of group member increases by one, the borrowers' loan repayment probability increases by 1.18 times. This result was the in parallel with study conducted by J.T.O. V. 
\section[{Summary and Conclusion}]{Summary and Conclusion}\par
The aim of Micro finance institutions is to give financial service, especially microcredit for the poor of poor and low income. In spite of the fact, women coverage of poor of poor and low income group was very high. Microfinance programs are currently being promoted by the government as well as nongovernmental organizations as a key approach for addressing both eradicating poverty and women's empowerment in Ethiopia. And micro credit have a positive impact to address the objective of micro finance institution as well as governmental policy to poverty alleviating and serving the poor, especially solving socio economic problem of women which leads to empowerment. Besides to this positive impact many studies shows that, loan repayment problem which is obstacle for both borrowers and lenders to achieve their objective. However, the main objective of this study was to identify the determinant factors that affect loan repayment performance of women borrowers from micro finance institution in south west Ethiopia.\par
The study result show that, 85(46.7\%) of borrowers are defaulted in loan repayment and 97(53.3\%) of respondents were non-defaulter. The figure indicate that the numbers of defaulters are almost nears to half of total borrowers. The respondents mention various reasons that contribute to their default in loan repayment; for example, lack of training and follow up, business idea does not work out, cash flow problems, failure in the business, lack of investing loan on intended purpose, in sufficiency of loan to start intended business/activities, the difference between amount of loan proposed by borrowers and approved by MFIs and interest rate is very high. Many of them are similar with the factors identified by this study. According to \hyperref[b16]{Remenyi (2000)}, subsidized credit and subsidized banking with the poor are inimical to "best practice in microfinance". Moreover, Microfinance Institutions also offered skills training and marketing to their clients, which is not in similar with our finding.\par
Over all this study identifies significant factors affecting loan repayment performance by using of econometric model. Of total independent variables that affect loan repayment performance, nine variables six variables were statistical significant. From six significant variables three of them are affect positively, namely using of loan for intended purpose, number of group member and sufficiency of loan to start business or sufficiency of loan for intended purpose and the remaining are affect negatively.\par
The age variable was negatively and significantly influencing loan repayment performance. A unit increase in the respondents' age decreases the probability of being defaulter by 0.173. This implies that through time aged respondents more responsible for their activities became settled and accumulate wealth more than youngsters. When borrowers educational level increased by attending one more year at school have the probability of increasing to becoming defaulter by 0.008 times. In short, there is inverse relationship between educational level and loan repayment. This did not conform to theoretical expectations in general and stated under literature as well as introduction of the study.\par
Sufficiency of loan to start business/for intended purpose increases the borrowers' loan repayment probability by 11.03 times. Therefore, these positive preconditions enable borrowers to enhance loan repayment performance better. Living in rural area were decreases the probability of being defaulter by 0.015 times compared to urban area. Using of loan for intended purpose or activity increases the borrowers' loan repayment performance probability by 3.32 times compared to those not investing/using for intended purpose. As indicated under binary logistic regression result, when number of group member increases by one, the borrowers' loan repayment performance probability increases by 1.18 times.\par
Finally, a determinant factor affecting loan repayment performance of women borrows from microfinance institution was identified with their different extent and significance. From the study result it is possible to conclude that, positive preconditions including training and better supervision enable borrowers to enhance loan repayment performance. The result indicate that, MFIs and other stockholders working for the beneficiary of women's must and essential to give attention on the specified problems. When women became defaulter they pay back the loan by selling any resource of the household that is enough to pay or by borrowing another loan from other Micro finance or from local money lenders with higher interest rate than microfinance. At the end of the day it leads the life of the whole family from hope to worthy than before.  
\section[{Annex i}]{Annex i}\begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.85\textwidth}}
Loan repayment performance Function of this study\\
defined as:\\
LRP= f (AG, ME, EL, TL, NHHM, NGM, SLFIP, ILIP, ALFMFI)\\
Where,\\
LRP= Loan repayment performance (Dependent\\
variable)\\
AG= Age\\
TL= Type of Locality\\
ME= Marital Status\\
EL= Educational Level\\
NGM= Number of Group Member\\
NHHM= Number of Household Member\\
ALFMFI= Amount of loan Approved by MFIs\\
SLIP= Sufficiency of Loan for intended purpose\\
ILIP= Investing Loan on intended purpose\\
IV.\end{longtable} \par
 
\caption{\label{tab_0}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.85\textwidth}}
2017\\
Year\\
Volume XVII Issue I Version I\\
( ) C\\
Global Journal of Management and Business Research\end{longtable} \par
 
\caption{\label{tab_1}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.1312158469945355\textwidth}P{0.18230874316939888\textwidth}P{0.11147540983606558\textwidth}P{0.09405737704918032\textwidth}P{0.054576502732240434\textwidth}P{0.1172814207650273\textwidth}P{0.07431693989071038\textwidth}P{0.08476775956284152\textwidth}}
\tabcellsep \tabcellsep \multicolumn{6}{l}{Did you face any Difficult to Repay Last Round Loan}\\
\multicolumn{2}{l}{Background Characteristics}\tabcellsep No\tabcellsep \tabcellsep Yes\tabcellsep \tabcellsep Total\tabcellsep \\
\tabcellsep \tabcellsep No.\tabcellsep \%\tabcellsep No.\tabcellsep \%\tabcellsep No.\tabcellsep \%\\
Marital status\tabcellsep Married\tabcellsep 71\tabcellsep 39\tabcellsep 71\tabcellsep 39\tabcellsep 142\tabcellsep 78\\
\tabcellsep Divorced\tabcellsep 25\tabcellsep 13.7\tabcellsep 4\tabcellsep 2.2\tabcellsep 29\tabcellsep 15.9\\
\tabcellsep Widowed\tabcellsep 1\tabcellsep 0.5\tabcellsep 10\tabcellsep 5.5\tabcellsep 11\tabcellsep 6\\
\tabcellsep Total\tabcellsep 97\tabcellsep 53.3\tabcellsep 85\tabcellsep 46.7\tabcellsep 182\tabcellsep 100\\
Age\tabcellsep 18-24\tabcellsep 4\tabcellsep 2.2\tabcellsep 6\tabcellsep 3.3\tabcellsep 10\tabcellsep 5.5\\
\tabcellsep 25-32\tabcellsep 79\tabcellsep 43.4\tabcellsep 59\tabcellsep 32.4\tabcellsep 138\tabcellsep 75.8\\
\tabcellsep 33-39\tabcellsep 10\tabcellsep 5.5\tabcellsep 17\tabcellsep 9.3\tabcellsep 27\tabcellsep 14.8\\
\tabcellsep 40-47\tabcellsep 4\tabcellsep 2.2\tabcellsep 3\tabcellsep 1.6\tabcellsep 7\tabcellsep 3.8\\
\tabcellsep Total\tabcellsep 97\tabcellsep 53.3\tabcellsep 85\tabcellsep 46.7\tabcellsep 182\tabcellsep 100\\
Educational level\tabcellsep No education\tabcellsep 59\tabcellsep 32.4\tabcellsep 74\tabcellsep 40.7\tabcellsep 133\tabcellsep 73.1\\
\tabcellsep Primary education\tabcellsep 30\tabcellsep 16.5\tabcellsep 11\tabcellsep 6\tabcellsep 41\tabcellsep 22.5\\
\tabcellsep Secondary and above\tabcellsep 8\tabcellsep 4.4\tabcellsep 0\tabcellsep 0\tabcellsep 8\tabcellsep 4.4\\
\tabcellsep Total\tabcellsep 97\tabcellsep 53.3\tabcellsep 85\tabcellsep 46.7\tabcellsep 182\tabcellsep 100\\
Number of household\tabcellsep <or equal to 5\tabcellsep 78\tabcellsep 42.9\tabcellsep 61\tabcellsep 33.5\tabcellsep 139\tabcellsep 76.4\\
\tabcellsep >5\tabcellsep 19\tabcellsep 10.4\tabcellsep 24\tabcellsep 13.2\tabcellsep 123\tabcellsep 23.6\\
\tabcellsep Total\tabcellsep 97\tabcellsep 53.3\tabcellsep 85\tabcellsep 46.7\tabcellsep 182\tabcellsep 100\\
Residence\tabcellsep Urban\tabcellsep 9\tabcellsep 4.9\tabcellsep 18\tabcellsep 9.9\tabcellsep 27\tabcellsep 14.8\\
\tabcellsep Rural\tabcellsep 88\tabcellsep 48.4\tabcellsep 67\tabcellsep 36.8\tabcellsep 155\tabcellsep 85.2\\
\tabcellsep Total\tabcellsep 97\tabcellsep 53.3\tabcellsep 85\tabcellsep 46.7\tabcellsep 182\tabcellsep 100\\
Household headship status\tabcellsep Female Male\tabcellsep 26 71\tabcellsep 14.3 39\tabcellsep 14 71\tabcellsep 7.7 39\tabcellsep 40 142\tabcellsep 22 78\\
\tabcellsep Total\tabcellsep 97\tabcellsep 53.3\tabcellsep 85\tabcellsep 46.7\tabcellsep 182\tabcellsep 100\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{3}{l}{Source: Survey result, 2015}\end{longtable} \par
 
\caption{\label{tab_2}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{P{0.17\textwidth}P{0.068\textwidth}P{0.204\textwidth}P{0.068\textwidth}P{0.272\textwidth}P{0.068\textwidth}}
No\tabcellsep \tabcellsep Yes\tabcellsep \tabcellsep Total\tabcellsep \\
No.\tabcellsep \%\tabcellsep No.\tabcellsep \%\tabcellsep No.\tabcellsep \%\end{longtable} \par
 
\caption{\label{tab_3}Table 2 :}\end{figure}
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