\documentclass[11pt,twoside]{article}\makeatletter

\IfFileExists{xcolor.sty}%
  {\RequirePackage{xcolor}}%
  {\RequirePackage{color}}
\usepackage{colortbl}
\usepackage{wrapfig}
\usepackage{ifxetex}
\ifxetex
  \usepackage{fontspec}
  \usepackage{xunicode}
  \catcode`⃥=\active \def⃥{\textbackslash}
  \catcode`❴=\active \def❴{\{}
  \catcode`❵=\active \def❵{\}}
  \def\textJapanese{\fontspec{Noto Sans CJK JP}}
  \def\textChinese{\fontspec{Noto Sans CJK SC}}
  \def\textKorean{\fontspec{Noto Sans CJK KR}}
  \setmonofont{DejaVu Sans Mono}
  
\else
  \IfFileExists{utf8x.def}%
   {\usepackage[utf8x]{inputenc}
      \PrerenderUnicode{–}
    }%
   {\usepackage[utf8]{inputenc}}
  \usepackage[english]{babel}
  \usepackage[T1]{fontenc}
  \usepackage{float}
  \usepackage[]{ucs}
  \uc@dclc{8421}{default}{\textbackslash }
  \uc@dclc{10100}{default}{\{}
  \uc@dclc{10101}{default}{\}}
  \uc@dclc{8491}{default}{\AA{}}
  \uc@dclc{8239}{default}{\,}
  \uc@dclc{20154}{default}{ }
  \uc@dclc{10148}{default}{>}
  \def\textschwa{\rotatebox{-90}{e}}
  \def\textJapanese{}
  \def\textChinese{}
  \IfFileExists{tipa.sty}{\usepackage{tipa}}{}
\fi
\def\exampleFont{\ttfamily\small}
\DeclareTextSymbol{\textpi}{OML}{25}
\usepackage{relsize}
\RequirePackage{array}
\def\@testpach{\@chclass
 \ifnum \@lastchclass=6 \@ne \@chnum \@ne \else
  \ifnum \@lastchclass=7 5 \else
   \ifnum \@lastchclass=8 \tw@ \else
    \ifnum \@lastchclass=9 \thr@@
   \else \z@
   \ifnum \@lastchclass = 10 \else
   \edef\@nextchar{\expandafter\string\@nextchar}%
   \@chnum
   \if \@nextchar c\z@ \else
    \if \@nextchar l\@ne \else
     \if \@nextchar r\tw@ \else
   \z@ \@chclass
   \if\@nextchar |\@ne \else
    \if \@nextchar !6 \else
     \if \@nextchar @7 \else
      \if \@nextchar (8 \else
       \if \@nextchar )9 \else
  10
  \@chnum
  \if \@nextchar m\thr@@\else
   \if \@nextchar p4 \else
    \if \@nextchar b5 \else
   \z@ \@chclass \z@ \@preamerr \z@ \fi \fi \fi \fi
   \fi \fi  \fi  \fi  \fi  \fi  \fi \fi \fi \fi \fi \fi}
\gdef\arraybackslash{\let\\=\@arraycr}
\def\@textsubscript#1{{\m@th\ensuremath{_{\mbox{\fontsize\sf@size\z@#1}}}}}
\def\Panel#1#2#3#4{\multicolumn{#3}{){\columncolor{#2}}#4}{#1}}
\def\abbr{}
\def\corr{}
\def\expan{}
\def\gap{}
\def\orig{}
\def\reg{}
\def\ref{}
\def\sic{}
\def\persName{}\def\name{}
\def\placeName{}
\def\orgName{}
\def\textcal#1{{\fontspec{Lucida Calligraphy}#1}}
\def\textgothic#1{{\fontspec{Lucida Blackletter}#1}}
\def\textlarge#1{{\large #1}}
\def\textoverbar#1{\ensuremath{\overline{#1}}}
\def\textquoted#1{‘#1’}
\def\textsmall#1{{\small #1}}
\def\textsubscript#1{\@textsubscript{\selectfont#1}}
\def\textxi{\ensuremath{\xi}}
\def\titlem{\itshape}
\newenvironment{biblfree}{}{\ifvmode\par\fi }
\newenvironment{bibl}{}{}
\newenvironment{byline}{\vskip6pt\itshape\fontsize{16pt}{18pt}\selectfont}{\par }
\newenvironment{citbibl}{}{\ifvmode\par\fi }
\newenvironment{docAuthor}{\ifvmode\vskip4pt\fontsize{16pt}{18pt}\selectfont\fi\itshape}{\ifvmode\par\fi }
\newenvironment{docDate}{}{\ifvmode\par\fi }
\newenvironment{docImprint}{\vskip 6pt}{\ifvmode\par\fi }
\newenvironment{docTitle}{\vskip6pt\bfseries\fontsize{22pt}{25pt}\selectfont}{\par }
\newenvironment{msHead}{\vskip 6pt}{\par}
\newenvironment{msItem}{\vskip 6pt}{\par}
\newenvironment{rubric}{}{}
\newenvironment{titlePart}{}{\par }

\newcolumntype{L}[1]{){\raggedright\arraybackslash}p{#1}}
\newcolumntype{C}[1]{){\centering\arraybackslash}p{#1}}
\newcolumntype{R}[1]{){\raggedleft\arraybackslash}p{#1}}
\newcolumntype{P}[1]{){\arraybackslash}p{#1}}
\newcolumntype{B}[1]{){\arraybackslash}b{#1}}
\newcolumntype{M}[1]{){\arraybackslash}m{#1}}
\definecolor{label}{gray}{0.75}
\def\unusedattribute#1{\sout{\textcolor{label}{#1}}}
\DeclareRobustCommand*{\xref}{\hyper@normalise\xref@}
\def\xref@#1#2{\hyper@linkurl{#2}{#1}}
\begingroup
\catcode`\_=\active
\gdef_#1{\ensuremath{\sb{\mathrm{#1}}}}
\endgroup
\mathcode`\_=\string"8000
\catcode`\_=12\relax

\usepackage[a4paper,twoside,lmargin=1in,rmargin=1in,tmargin=1in,bmargin=1in,marginparwidth=0.75in]{geometry}
\usepackage{framed}

\definecolor{shadecolor}{gray}{0.95}
\usepackage{longtable}
\usepackage[normalem]{ulem}
\usepackage{fancyvrb}
\usepackage{fancyhdr}
\usepackage{graphicx}
\usepackage{marginnote}

\renewcommand{\@cite}[1]{#1}


\renewcommand*{\marginfont}{\itshape\footnotesize}

\def\Gin@extensions{.pdf,.png,.jpg,.mps,.tif}

  \pagestyle{fancy}

\usepackage[pdftitle={Empirical Analysis on Determinants of Child labor and Associated Problems (The Case of Damot Gale District, Wolaita Zone, SNNPR, Ethiopia)},
 pdfauthor={}]{hyperref}
\hyperbaseurl{}

	 \paperwidth210mm
	 \paperheight297mm
              
\def\@pnumwidth{1.55em}
\def\@tocrmarg {2.55em}
\def\@dotsep{4.5}
\setcounter{tocdepth}{3}
\clubpenalty=8000
\emergencystretch 3em
\hbadness=4000
\hyphenpenalty=400
\pretolerance=750
\tolerance=2000
\vbadness=4000
\widowpenalty=10000

\renewcommand\section{\@startsection {section}{1}{\z@}%
     {-1.75ex \@plus -0.5ex \@minus -.2ex}%
     {0.5ex \@plus .2ex}%
     {\reset@font\Large\bfseries}}
\renewcommand\subsection{\@startsection{subsection}{2}{\z@}%
     {-1.75ex\@plus -0.5ex \@minus- .2ex}%
     {0.5ex \@plus .2ex}%
     {\reset@font\Large}}
\renewcommand\subsubsection{\@startsection{subsubsection}{3}{\z@}%
     {-1.5ex\@plus -0.35ex \@minus -.2ex}%
     {0.5ex \@plus .2ex}%
     {\reset@font\large}}
\renewcommand\paragraph{\@startsection{paragraph}{4}{\z@}%
     {-1ex \@plus-0.35ex \@minus -0.2ex}%
     {0.5ex \@plus .2ex}%
     {\reset@font\normalsize}}
\renewcommand\subparagraph{\@startsection{subparagraph}{5}{\parindent}%
     {1.5ex \@plus1ex \@minus .2ex}%
     {-1em}%
     {\reset@font\normalsize\bfseries}}


\def\l@section#1#2{\addpenalty{\@secpenalty} \addvspace{1.0em plus 1pt}
 \@tempdima 1.5em \begingroup
 \parindent \z@ \rightskip \@pnumwidth 
 \parfillskip -\@pnumwidth 
 \bfseries \leavevmode #1\hfil \hbox to\@pnumwidth{\hss #2}\par
 \endgroup}
\def\l@subsection{\@dottedtocline{2}{1.5em}{2.3em}}
\def\l@subsubsection{\@dottedtocline{3}{3.8em}{3.2em}}
\def\l@paragraph{\@dottedtocline{4}{7.0em}{4.1em}}
\def\l@subparagraph{\@dottedtocline{5}{10em}{5em}}
\@ifundefined{c@section}{\newcounter{section}}{}
\@ifundefined{c@chapter}{\newcounter{chapter}}{}
\newif\if@mainmatter 
\@mainmattertrue
\def\chaptername{Chapter}
\def\frontmatter{%
  \pagenumbering{roman}
  \def\thechapter{\@roman\c@chapter}
  \def\theHchapter{\roman{chapter}}
  \def\thesection{\@roman\c@section}
  \def\theHsection{\roman{section}}
  \def\@chapapp{}%
}
\def\mainmatter{%
  \cleardoublepage
  \def\thechapter{\@arabic\c@chapter}
  \setcounter{chapter}{0}
  \setcounter{section}{0}
  \pagenumbering{arabic}
  \setcounter{secnumdepth}{6}
  \def\@chapapp{\chaptername}%
  \def\theHchapter{\arabic{chapter}}
  \def\thesection{\@arabic\c@section}
  \def\theHsection{\arabic{section}}
}
\def\backmatter{%
  \cleardoublepage
  \setcounter{chapter}{0}
  \setcounter{section}{0}
  \setcounter{secnumdepth}{2}
  \def\@chapapp{\appendixname}%
  \def\thechapter{\@Alph\c@chapter}
  \def\theHchapter{\Alph{chapter}}
  \appendix
}
\newenvironment{bibitemlist}[1]{%
   \list{\@biblabel{\@arabic\c@enumiv}}%
       {\settowidth\labelwidth{\@biblabel{#1}}%
        \leftmargin\labelwidth
        \advance\leftmargin\labelsep
        \@openbib@code
        \usecounter{enumiv}%
        \let\p@enumiv\@empty
        \renewcommand\theenumiv{\@arabic\c@enumiv}%
	}%
  \sloppy
  \clubpenalty4000
  \@clubpenalty \clubpenalty
  \widowpenalty4000%
  \sfcode`\.\@m}%
  {\def\@noitemerr
    {\@latex@warning{Empty `bibitemlist' environment}}%
    \endlist}

\def\tableofcontents{\section*{\contentsname}\@starttoc{toc}}
\parskip0pt
\parindent1em
\def\Panel#1#2#3#4{\multicolumn{#3}{){\columncolor{#2}}#4}{#1}}
\newenvironment{reflist}{%
  \begin{raggedright}\begin{list}{}
  {%
   \setlength{\topsep}{0pt}%
   \setlength{\rightmargin}{0.25in}%
   \setlength{\itemsep}{0pt}%
   \setlength{\itemindent}{0pt}%
   \setlength{\parskip}{0pt}%
   \setlength{\parsep}{2pt}%
   \def\makelabel##1{\itshape ##1}}%
  }
  {\end{list}\end{raggedright}}
\newenvironment{sansreflist}{%
  \begin{raggedright}\begin{list}{}
  {%
   \setlength{\topsep}{0pt}%
   \setlength{\rightmargin}{0.25in}%
   \setlength{\itemindent}{0pt}%
   \setlength{\parskip}{0pt}%
   \setlength{\itemsep}{0pt}%
   \setlength{\parsep}{2pt}%
   \def\makelabel##1{\upshape ##1}}%
  }
  {\end{list}\end{raggedright}}
\newenvironment{specHead}[2]%
 {\vspace{20pt}\hrule\vspace{10pt}%
  \phantomsection\label{#1}\markright{#2}%

  \pdfbookmark[2]{#2}{#1}%
  \hspace{-0.75in}{\bfseries\fontsize{16pt}{18pt}\selectfont#2}%
  }{}
      \def\TheFullDate{2017-01-15 (revised: 15 January 2017)}
\def\TheID{\makeatother }
\def\TheDate{2017-01-15}
\title{Empirical Analysis on Determinants of Child labor and Associated Problems (The Case of Damot Gale District, Wolaita Zone, SNNPR, Ethiopia)}
\author{}\makeatletter 
\makeatletter
\newcommand*{\cleartoleftpage}{%
  \clearpage
    \if@twoside
    \ifodd\c@page
      \hbox{}\newpage
      \if@twocolumn
        \hbox{}\newpage
      \fi
    \fi
  \fi
}
\makeatother
\makeatletter
\thispagestyle{empty}
\markright{\@title}\markboth{\@title}{\@author}
\renewcommand\small{\@setfontsize\small{9pt}{11pt}\abovedisplayskip 8.5\p@ plus3\p@ minus4\p@
\belowdisplayskip \abovedisplayskip
\abovedisplayshortskip \z@ plus2\p@
\belowdisplayshortskip 4\p@ plus2\p@ minus2\p@
\def\@listi{\leftmargin\leftmargini
               \topsep 2\p@ plus1\p@ minus1\p@
               \parsep 2\p@ plus\p@ minus\p@
               \itemsep 1pt}
}
\makeatother
\fvset{frame=single,numberblanklines=false,xleftmargin=5mm,xrightmargin=5mm}
\fancyhf{} 
\setlength{\headheight}{14pt}
\fancyhead[LE]{\bfseries\leftmark} 
\fancyhead[RO]{\bfseries\rightmark} 
\fancyfoot[RO]{}
\fancyfoot[CO]{\thepage}
\fancyfoot[LO]{\TheID}
\fancyfoot[LE]{}
\fancyfoot[CE]{\thepage}
\fancyfoot[RE]{\TheID}
\hypersetup{citebordercolor=0.75 0.75 0.75,linkbordercolor=0.75 0.75 0.75,urlbordercolor=0.75 0.75 0.75,bookmarksnumbered=true}
\fancypagestyle{plain}{\fancyhead{}\renewcommand{\headrulewidth}{0pt}}

\date{}
\usepackage{authblk}

\providecommand{\keywords}[1]
{
\footnotesize
  \textbf{\textit{Index terms---}} #1
}

\usepackage{graphicx,xcolor}
\definecolor{GJBlue}{HTML}{273B81}
\definecolor{GJLightBlue}{HTML}{0A9DD9}
\definecolor{GJMediumGrey}{HTML}{6D6E70}
\definecolor{GJLightGrey}{HTML}{929497} 

\renewenvironment{abstract}{%
   \setlength{\parindent}{0pt}\raggedright
   \textcolor{GJMediumGrey}{\rule{\textwidth}{2pt}}
   \vskip16pt
   \textcolor{GJBlue}{\large\bfseries\abstractname\space}
}{%   
   \vskip8pt
   \textcolor{GJMediumGrey}{\rule{\textwidth}{2pt}}
   \vskip16pt
}

\usepackage[absolute,overlay]{textpos}

\makeatother 
      \usepackage{lineno}
      \linenumbers
      
\begin{document}

             \author[1]{Zegeye Paulos  Borko}

             \affil[1]{  Wolaita Sodo University}

\renewcommand\Authands{ and }

\date{\small \em Received: 10 December 2016 Accepted: 31 December 2016 Published: 15 January 2017}

\maketitle


\begin{abstract}
        


The study was carried out at Damot Gale district of Wolaita zone in Southern nation nationalities regional state with the main objectives to describe factors of child labor in the study area. In order to attain this objective the study made use of cross-sectional household survey data collected from 94 sample households .The data collected were analyzed and discussed by using descriptive statistics and binary logit regression model. To this end, identifying children?s who were in child labor and those who were not in child labor; descriptive result shows that from different age category a total of 69(73%) of the children?s were engaged in different activity and the remaining 25(27%) responded as not working. Most children started working below the age of 8 and major sectors of work were unpaid family work such as agriculture (58% M &7.24 % F ) and Home service (7.3% F & 22% M). The result of the logistic regression model revealed that out of 8 variables included in the model, 4 explanatory variables were found to be significant at 1percent, 5percent and 10% percent level. Accordingly, family size and loss of parents were found to have positive association with child labor and statistically significant up less than 1% and 10% respectively. Meanwhile occupation status of the parents and education level of were found out to have strong negative association with child labor and statistically significant up to less than 1 percent level of significance.

\end{abstract}


\keywords{child labor, logit model, damot gale district.}

\begin{textblock*}{18cm}(1cm,1cm) % {block width} (coords) 
\textcolor{GJBlue}{\LARGE Global Journals \LaTeX\ JournalKaleidoscope\texttrademark}
\end{textblock*}

\begin{textblock*}{18cm}(1.4cm,1.5cm) % {block width} (coords) 
\textcolor{GJBlue}{\footnotesize \\ Artificial Intelligence formulated this projection for compatibility purposes from the original article published at Global Journals. However, this technology is currently in beta. \emph{Therefore, kindly ignore odd layouts, missed formulae, text, tables, or figures.}}
\end{textblock*}


\let\tabcellsep& 	 	 		 \par
Introduction sustaining a livelihood. There are at least three reasons why agricultural production based on fair trade schemes can better tackle the problem of exploitative child labor. Firstly, control mechanisms are much more elaborated. Secondly, the shorter supply chain allows traceability, an important precondition for an efficient control system. Thirdly, a guaranteed minimum price prevents absolute poverty a major cause of child labor -while the fair trade premium can be used to overcome long-term structural barriers, thus addressing the root causes of exploitative child labor \hyperref[b3]{(Etzensperger, 2012)}.\par
Of all children engaged in economic activities outside the house or household work, about 88 percent reside in rural areas. The highest rates of child labor are to be found in SNNP and Oromia (88.8 percent in each). The agriculture sector accounts the largest portion, which is 75 percent and service sector 12 percent and four percent in manufacturing \hyperref[b6]{(Guarcello \& Rosati, 2007)}. This ranks the country among one of the countries with highest rates of child labor in the world. Poverty, lack of educational opportunity parent's choice, children's choice and societal attitudes are main factors in the Ethiopia \hyperref[b8]{(Habtamu, 2011)}.Since studies done in Ethiopia on child labor are far from adequate, and almost all of them were conducted in the major urban settings of the country, the rural community where high prevalence of the problem is expected is not yet studied. Therefore this particular study was done with the objective of determining the magnitude of child labor and associated problems in Damot Gale district. 
\section[{b) Objectives i. General Objective}]{b) Objectives i. General Objective}\par
The main objective of the study is to assess the magnitude of child labor and associated problems in the case of Damot Gale district.\par
ii. Specific Objectives The study has the following specific objectives:\par
-To assess the challenges and abusive practices that these working children face -To investigate the causes for the involvement of children in the labor market; Empirical Analysis on Determinants of Child labor and Associated Problems (The Case of Damot Gale District, Wolaita Zone, SNNPR, Ethiopia) a) Back ground of the study ccording to the ILO, child labor is "work that deprives children of their childhood, their potential and their dignity, and that is harmful to physical and mental development."The ILO classifies work conducted by children into three categories: children in employment, child labor, and hazardous work. The ILO condemns both child labor and hazardous work with the goal of eliminating hazardous child labor by 2016.\par
Child labor is often characterized as pernicious and evil, something that has to be unequivocally rejected. Around 60\% of all exploitative child labor takes place in agriculture, the (labor) dominant sector in LDCs. Children in rural regions of LDCs are often the only available "means" for producing agricultural goods and A II. 
\section[{Methodology a) Description of the Study Area}]{Methodology a) Description of the Study Area}\par
Damot Gale is one of 12 districts in Wolaita zone of SNNPR of Ethiopia. It is located at 139km southwest of the Hawassa town which is the capital of southern regional state and 365km from Addis Ababa in the southern direction. Geographically, it is located between 6 0 53'-7 0 6' 30" north latitude and 37 0 46' -37 0 58' 40" east longitude. It has an altitude ranging from1501-2950 meters above mean sea level. Mount Damota is the highest peak in the area. The study area covers an area of 24285.861 hectare. Damot Gale district is divided into three basic traditional agroclimatic zones such as Dega or highland (25.3\%), Woina dega or midland (61.2\%) and Kola or lowland (13\%) (WARD office, 2014). Woina dega dominates the study area which has bimodal distribution of rainfall. Mean annual rainfall ranges between 1001-1400 mm (RFEDB, 
\section[{2013) as cited in}]{2013) as cited in}\par
The study area is bordered on the south west by Sodo zuria, on the northwest by Boloso sore and Damot Pulassa, on the north by Hadiya zone, on the east by Duguna Fango, and on the south east by Damot woyde. Based on the CSA (2011) estimation and district finance and economy development office report, Damot Gale has a population of 177,570 out of this103,011 are male and 74,559 are female. The total households of the district are 30,767 male households 26,417 and female 4,350.and has a total of 31 rural kebels. Children's age less than 15 year in the four kebele is 3148. Like other parts of the region agriculture is the main means of livelihood for the population both in terms of crop production and livestock. 
\section[{b) Data Type and Source}]{b) Data Type and Source}\par
The research work was mainly used a primary data which was collected from the study area. Information on the demographic and socio economic condition of the children's and their family data was collected through structured questionnaires having a close ended elicitation format with open ended follow up questions. The structured questionnaires were posted to the children's and to their parents with face to face interviews. The purpose of the study was explained to the study subjects and their willingness to participate in the study asked before conducting interviews. Interviews were conducted during late afternoons and evenings as these were found to be optimal time to find children at home. Child labor: economic exploitation and any other circumstance under which children perform paid or unpaid work that might be directly detrimental to their development, or that might prevent them from exercising their education, health and leisure in the study area.\par
Secondary data was used to supplement the primary data and obtained from different published and unpublished documents. Both quantitative and qualitative methods were employed about because these two types of data complement each other. Since the main aim of this research was mainly investigating statically the relation between dependant variable i.e. child labor and explanatory (independent) variables that determine child labor in the study area. In addition to statistical investigation key informant and focus group discussion were taken place. 
\section[{c) Sample Size Determination}]{c) Sample Size Determination}\par
The sample size in this study was determined by using the minimum sample size formula of Fowler(2001) and then adjusted for the total population of the study area by Cochran's sample size formula \hyperref[b1]{(Cochran, 1977)} as shown below.\par
Where, n=sample size, d=the level of risk the researcher has been decided to take that true margin of error may exceed the acceptable margin of error=0.06 (precision 6\%) with confidence level 94\%. 
\section[{d) Sampling Technique}]{d) Sampling Technique}\par
The study population constituted of children aged 5-15 years residing in Damot Gale district in four kebels during the study period. For this study, multistage sampling procedures was used. In the first stage, Damot Gale district was selected purposively because it was one of the densely populated districts in zone level as well as regional level. In the second stage three kebels were selected from 31 kebels by simple random sampling. At the third stage, Systematic sampling technique was employed to identify the study subjects. The first household was selected by lottery method; a child from every 4th house was interviewed. If the number of children was above one in a household, only one child was selected by lottery method. If no child was found in the selected household, the next Therefore, the final sample size for the district becomes 94 which was the sum of four kebels as we can see in the table below. household with a child was visited before going to the next fourth household. As the study was household based, homeless street children were not included in the study. 
\section[{e) Data Collection Techniques and Instruments}]{e) Data Collection Techniques and Instruments}\par
The researcher used kebele agriculture development workers to collect primary source data. Before entering to survey, the development agents were given a training mainly focusing on the contents of the questionnaire and procedure of survey. Observation and discussion with district as well as kebele governmental officials and expertise was held by the researcher. The key points were prepared for discussion with key informant, employers and governmental officials. Structured and semi-structured interview questionnaires were designed to collect quantitative data. 
\section[{f) Method of Data Analysis}]{f) Method of Data Analysis}\par
To achieve the objectives of this study, different methods of data analysis were used. The study used both descriptive and econometric analysis. The descriptive analysis uses percentages, graphs, tabulations and multiple linear regression analysis was used to identify the effect of determinants of child labor. Tools and statistics used in descriptive and econometric are generated with the help of econometric software STATA version 11. 
\section[{Binary Logit Model}]{Binary Logit Model}\par
In order to identify the determinants of child labor in the study area a binary response model is specified to be estimated by logit regression technique. The logit specification is designed to analyze qualitative data reflecting a choice between two alternatives, which in this case1 for being in child labor and 0 otherwise. The parameters of this model will be estimated by using the maximum likelihood estimation rather than the movement estimation in which OLS regression technique rely on. The logit method gives parameter estimates that are asymptotically efficient, and consistent. Indeed, the logit approach is known to produce statistically sound results \hyperref[b7]{(Gujarati \& porter, 2009}) Probability of being in child labor is specified as the value of the cumulative distribution function which is specified as function of the explanatory variables. The equation is of the form: Where, Y= probability of being in child labor or not in child labor Coefficient of the explanatory variables. Xi= Explanatory variable.\par
For the case of a single independent variable, the logistic regression model can be written as 
\section[{Or equivalently}]{Or equivalently}\par
Where, ?0 and ?1 are coefficients to be estimated from data, Xi is the independent variable e is the base of the natural logarithm.\par
For ease of exposition the model can be written as (for more than one independent variables) 
\section[{Or equivalently}]{Or equivalently}\par
This particular study was deal about the probability of being in child labor or not and this expression expressed in mathematical form as follows:\par
The probability of a child being in labor market (an event occurring) as the form: Note: The error term also follows logistic distribution For a non-event (not in child labor) cumulative logistic distribution, representing the probability is just (1-pi) i.e.\par
Therefore, by dividing equation (2.5) by equation (2.7) we can result in the odds-ratio in binary response, which is as stated below:\par
Equation (2.8) is simply the odd-ratio in favor of a children is falling labor exploitation. This is the ratio of the probability of a children will be in labor market to the probability that it will not be in child labor.\par
When we take the natural logarisim of odd-ratio of equation (  {\ref 2}  
\section[{Results and Discussions}]{Results and Discussions}\par
This chapter deals with the results of descriptive statistics and binary logistic regression results of the causes for the involvement of children in the labor market; The analysis was done in light of the objectives of the study. Section 3.1 deals with descriptive analysis and section 3.2 presents the results of the econometric analysis. 
\section[{a) Descriptive Analysis}]{a) Descriptive Analysis}\par
In this study a total of 94 children's family or their parents were interviewed concerning child labor and associated factors that lead to exploitation of child labor. Majority of the head of the household were male 71(76\%) and the remaining 23(24\%) were female parents of the children who were sampled for this study. 86(91.4\%) of respondents were farmers, 2(2.1\%) government employee, 3(3.1\%), Daily laborers 1(1.06\%) andmerchants2(2.1\%).From the number of children's engaged in different activities children whose parents occupation was agriculture ranked first in number, followed by merchants and daily laborers and less in parents whose occupation was government employee. This is due to the fact that the government employees' access to participate/ easily understanding of different child related laws either by reading or attending in meeting than the rest of parents whose occupation was farming, merchant and daily laborers.  
\section[{i. Age and child labor}]{i. Age and child labor}\par
The table \hyperref[tab_4]{3}.1 below shows that the number of children in age category 5-8 accounts 27 (28.7\%) and, age 9-11 the number of children 38 (40.5\%) and age category 12-15 the number of children 29 (30.8\%). Accordingly, the age category 9-11ranked first in working which was 29 (42\%), followed by age category 12-15 the number of children who were responded as working were 24 (35\%) and the remaining 16 (23\%) were   
\section[{Schooling of the Children's}]{Schooling of the Children's}\par
The educational status of the children's was one of the variables under consideration of this study. The result of the survey shows that 68 (73\%) of the children's were currently in the school, 22 (23\%) never went to school and 4 (4\%) students dropout in order to support their parents with different activities. As the focus group discussion members confirmed that the two major reasons for not attending school and for dropping were the need to work and financial problem to cover school expenses an d those children's who were attending school were also working different activities before and after school. 
\section[{b) Model Analysis}]{b) Model Analysis}\par
To identify the major determinants of child labor in the study area dependent variable i.e. Probability of being in child labor was regressed against various explanatory variables. The regression table revealed that binary logistic model managed to predict 69 percent of the responses correctly. Apart from percent correct predictions, the model Chi-Square with "n" degrees of freedom and Hosmer and Lemeshow's were used to test goodness-of-fit test. Accordingly, p-values associated the Chi-Square with 8 degrees of freedom. The value of .0000 indicates that the model as a whole is statistically significant that shows the model fit the data well (see table \hyperref[tab_4]{3} 
\section[{.3 below)}]{.3 below)}\par
The variables included in the model were tested for the existence of multi-co linearity, if any. Contingency coefficient and variance inflation factor were used for multi-collinearity test of discrete and continuous variables, respectively. Contingency coefficient value ranges between 0 and 1, and as a rule of thumb variable with contingency coefficient below 0.75 shows weak association and value above it indicates strong association of variables. The contingency coefficient for the discrete variables included in the model was less than 0.75 that didn't suggest multi-collinearity to be a serious concern. As a common practice continuous variable having variance inflation factor of less than 10 believed to have no multi-collinearity and those with VIF of above 10 are subjected to the problem and should be excluded from the model \hyperref[b7]{(Gujarati, 2009)}.  ii. 
\section[{Volume XVII Issue V Version I}]{Volume XVII Issue V Version I} 
\section[{Table 3.2:}]{Table 3.2:}\par
Global Journal of Human Social Science -Year 2017\par
In logistic regression analyses the variables that were positively related with the probability of being in child labor were household head sex, age, family size, dependency ratio, distance from school and loss of parents. The variables that were negatively correlated with the probability of being in child labor were head education and occupation of parents.\par
In the table above 8 explanatory variables were included in the model , 4 of the variables: household family size, distance from school, parents educational status and occupation have a significant effect on children's failing in to child labor at the significance level at 1 percent, 5 percent and 10 percent. The negative values of explanatory variables in the table above indicate that when the unit change in independent variable lead to decrease in probability of being in child labor. 
\section[{Marginal Effect for Logit Regression}]{Marginal Effect for Logit Regression}\par
In order to identify the factors of child labor in the study area this study employed the logit model. Since the model used is not linear, the marginal effect of each independent variable on the dependant variable is not constant but it depends on the value of the independent variables. Thus, marginal effects can be a means for summarizing how change in a response is related to change in a covariate. For dummy variables, the effects of discrete changes are computed, i.e., the marginal effects for dummy variables show how P(Y = 1) is predicted to change as Xk changes from 0 to 1 holding all other Xs equal.\par
Whereas for continuous independent variables, the marginal effect measures the instantaneous rate of change, i.e. we compute them for a variable while all other variables are held constant that means in this study change in the probability of being in child labor with a unit change in continuous independent variable \hyperref[b5]{(Greene, 1993)}.Thus, opposed to linear regression case, it is not possible to interpret the estimated parameters as the effect of the independent variable up on being in child labor. However, it is possible to compute the marginal effects at some interesting values of the significant explanatory variables. As we can see in table \hyperref[tab_4]{3} a. Family Size and child labor The size of household was found positive in this finding and the coefficient is statistically significant at 5percent level. Other things remain constant, as it was expected that household family size increase by a unit, the probability of a child to be included in child labor increase by about 17.75 percent. This is due to the fact that additional household member shares the limited resources that lead the household to become poor and expose the children's of that family to child labor in order to fill their consumption short fall.\par
b. Loss of Parents/ divorce and Child labor One of the determinants of child labor in the study area was loss of parents by different reason i.e by death, divorce etc. The variable is positively related with the probability of the child to be in child labor and the coefficient is statistically significant at 1 percent level. Holding all the model variables at their mean value, change from having parent status to no parents' status the probability of a children being in child labor increase by 58.9 percent. The possible explanation for this finding was Children who have broken family and those who lost parents early were forced to work for survival. A family with complain (mother and father), dysfunctional family, unproductive family, divorce, inability of mother or father or both by natural and artificial cases, and death of either father or mother or both by any cases increase the vulnerability of children to work. 
\section[{c. Education level of the households and child labor}]{c. Education level of the households and child labor}\par
The level of education is negatively correlated to the probability of a children being in child labor and the coefficient was statistically different from zero at 1 percent significance level. As it was expected, other things remain constant when the education level of parents increased by one unites the probability of children to fail in to the child labor decrease by 4.2 i.\par
percent. This is due to the fact that educated parents can capture easily any awareness creation to child labor and related problems and protect their children from child labor. 
\section[{d. Occupation of parents and child labor}]{d. Occupation of parents and child labor}\par
Occupation of parents is one of the factors determining child labor in the study area. The variable is negatively related to the probability of a children being in child labor and the coefficient was statistically significant at 1 percent level. Holding other variables constant change from no occupation status to occupation status of parents the probability of a children being in child labor decrease by about 67.64 percent.\par
This may be the case that the parents who have access to occupation that can feed their family have better chance of keeping their children's from child labor exploitation than those who were dependant on others(it may be relatives, government etc) 
\section[{IV. Conclusion and Recommendations a) Conclusion}]{IV. Conclusion and Recommendations a) Conclusion}\par
This study analyzed the child labor and factors affecting child in the study area. The study used 94 sample parents of the children were included and which was collected from four kebeles i.e. Damot Mokonisa, Gacheno, Shasha Gale and Wandara Gale. To attain the stated objectives of the study we used both descriptive and Econometric model analysis i.e. binary logit model.\par
Despite several limitations like exclusion of homeless street children who were expected to encounter the worst forms of child labor, lack of uniformity in the definition of various forms of child labor and limited sample size, this study has tried to show the magnitude of child labor in a rural community setting.\par
The study showed that nearly 73\% of the interviewed children were working and only 27\% were not working. Result of this findings shows that most of the children were started working below the age of 8 and major sectors of working were unpaid family work such as agriculture(58\% M\&7.24 \% F ) ,Home service (7.3\% F \& 22\% M)agriculture and home services were activates that children's were practicing.\par
The binary logit regression result shows that out of 8 variables included in this study to determine the influence of explanatory variable on probability of child to be failed in child labor4 variables were found significant at 1 percent, 5 percent and 10 percent level. Among the significant variables family size and loss of parents by different reason positively affect the probability of a child to be in child labor. On the other hand parent's occupational status and educational level affects negatively the probability of a child being in labor market.\par
Volume XVII Issue V Version I The desired areas of intervention stated by the subjects included family support education to children, raising community awareness about child labor, family planning, legislation, and implementation of law enforcement on child labor. ? Implementation of family planning and related measures should be taken to limit household family size. ? Creating job opportunity for parents can reduce child labor, large government infra structure construction such as rural roads, water, building constructions and others one way of creating job opportunities. ? In this study educational status of parents have negatively correlated to child labor exploitation. Education may be the most important tool to reduce child labor exploitation and it needs the close follow up of parents education implementation in rural area in order to hand over the problems related with child labor.\begin{figure}[htbp]
\noindent\textbf{1}\includegraphics[]{image-2.png}
\caption{\label{fig_0}Figure 1}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{58}\includegraphics[]{image-3.png}
\caption{\label{fig_1}from the age category 5 - 8 .}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-4.png}
\caption{\label{fig_3}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.25377697841726615\textwidth}P{0.21097122302158275\textwidth}P{0.2660071942446043\textwidth}P{0.11924460431654675\textwidth}}
Year 2017\tabcellsep \tabcellsep \tabcellsep \\
18\tabcellsep \tabcellsep \tabcellsep \\
Volume XVII Issue V Version I\tabcellsep \tabcellsep \tabcellsep \\
E )\tabcellsep \tabcellsep \tabcellsep \\
(\tabcellsep \tabcellsep \tabcellsep \\
-Global Journal of Human Social Science\tabcellsep Name of kebels Damot Mokonissa Gacheno Wandara Gale Shasha Gale Total\tabcellsep Total Population Total Children age < 15 5212 834 3702 629 6073 971 4200 714 19190 3148\tabcellsep Sample children age < 15 25 19 29 21 94\end{longtable} \par
 
\caption{\label{tab_0}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.008082408874801902\textwidth}P{0.8419175911251982\textwidth}}
III.\tabcellsep Year 2017\\
\tabcellsep Assumptions of Logistic Model\\
\tabcellsep 1. Assumes a linear relationship between the logit of\\
\tabcellsep the independent variable and dependant variables,\\
\tabcellsep however, does not assume a linear relationship\\
i.\tabcellsep between the actual dependant and independent\\
\tabcellsep variable\\
\tabcellsep 2. Independent variables were not linear functions of\\
\tabcellsep each other, i.e. perfect multicollinearity makes\\
\tabcellsep estimation impossible.\\
\tabcellsep 3. The model was correctly specified i.e.\\
\tabcellsep ? The true conditional probabilities are a logistic\\
\tabcellsep function of the independent variables;\\
\tabcellsep ? No important variables are omitted;\\
\tabcellsep ? No extraneous variables are included; and\\
\tabcellsep ? The independent variables are measured\\
\tabcellsep without error.\end{longtable} \par
 
\caption{\label{tab_1}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{11} \par 
\begin{longtable}{P{0.020777777777777777\textwidth}P{0.38911111111111113\textwidth}P{0.3551111111111111\textwidth}P{0.03966666666666667\textwidth}P{0.04533333333333334\textwidth}}
No.\tabcellsep Variables\tabcellsep \tabcellsep Measurement\tabcellsep Expected Signs\\
1\tabcellsep Occupation of parents (Ocu)\tabcellsep \multicolumn{2}{l}{Dummy (takes= 1 if parent has occup; otherwise 0)}\tabcellsep -\\
2\tabcellsep Dependancy Ratio (DR)\tabcellsep \multicolumn{2}{l}{Continues}\tabcellsep +\\
3\tabcellsep Distance from School (DitSch)\tabcellsep \multicolumn{2}{l}{Continues}\tabcellsep +\\
4\tabcellsep Loss of Parents/ Broken Marriages/\tabcellsep \multicolumn{2}{l}{Dummy (takes= 1 if broken/lost parent;}\tabcellsep +\\
\tabcellsep Divorsce (Los Pa)\tabcellsep \multicolumn{2}{l}{otherwise 0)}\\
5\tabcellsep Family Size (FS)\tabcellsep \multicolumn{2}{l}{Continues}\tabcellsep +\\
6\tabcellsep Sex\tabcellsep \multicolumn{2}{l}{Categorial (1 stands for Male; 0 for Female)}\tabcellsep -/+\\
7\tabcellsep Age (Ag)\tabcellsep \multicolumn{2}{l}{Continues}\tabcellsep +\\
8\tabcellsep Parents Education (Edu)\tabcellsep \multicolumn{2}{l}{Continues}\tabcellsep -\\
\tabcellsep \multicolumn{3}{l}{Respondants}\\
\tabcellsep \multicolumn{2}{l}{Male}\tabcellsep Female\\
\tabcellsep \tabcellsep \tabcellsep 24\%\\
\tabcellsep 76\%\tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_2}Table 1 . 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{31} \par 
\begin{longtable}{P{0.03411371237458194\textwidth}P{0.23026755852842806\textwidth}P{0.1620401337792642\textwidth}P{0.07675585284280936\textwidth}P{0.1108695652173913\textwidth}P{0.05969899665551839\textwidth}P{0.12792642140468227\textwidth}P{0.04832775919732441\textwidth}}
No.\tabcellsep Age\tabcellsep No,\tabcellsep \%\tabcellsep \multicolumn{2}{l}{Working No. \%}\tabcellsep \multicolumn{2}{l}{Not Working No. \%}\\
1\tabcellsep 5-8\tabcellsep 27\tabcellsep 28.7\tabcellsep 16\tabcellsep 23\tabcellsep 11\tabcellsep 44\\
2\tabcellsep 9-11\tabcellsep 38\tabcellsep 40.5\tabcellsep 29\tabcellsep 42\tabcellsep 9\tabcellsep 36\\
3\tabcellsep 12-15\tabcellsep 29\tabcellsep 30.8\tabcellsep 24\tabcellsep 35\tabcellsep 5\tabcellsep 20\\
\tabcellsep Total\tabcellsep 94\tabcellsep 100\tabcellsep 69\tabcellsep 100\tabcellsep 24\tabcellsep 100\\
\tabcellsep \tabcellsep \multicolumn{4}{l}{Educational status of the children's}\tabcellsep \tabcellsep \\
No.\tabcellsep Schooling\tabcellsep No.\tabcellsep \%\tabcellsep \multicolumn{2}{l}{Working \%}\tabcellsep \multicolumn{2}{l}{Not Working \%}\\
1\tabcellsep Currently in school\tabcellsep 68\tabcellsep 73\tabcellsep 53\tabcellsep 77\tabcellsep 15\tabcellsep 60\\
2\tabcellsep Never went to school\tabcellsep 22\tabcellsep 23\tabcellsep 12\tabcellsep 17.2\tabcellsep 10\tabcellsep 40\\
3\tabcellsep Drop Out\tabcellsep 4\tabcellsep 4\tabcellsep 4\tabcellsep 5.8\tabcellsep 0\tabcellsep 0\\
\tabcellsep Total\tabcellsep 94\tabcellsep 100\tabcellsep 69\tabcellsep 100\tabcellsep 25\tabcellsep 100\end{longtable} \par
 
\caption{\label{tab_3}Table 3 . 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{P{0.36494565217391306\textwidth}P{0.035416666666666666\textwidth}P{0.17092391304347826\textwidth}P{0.12164855072463768\textwidth}P{0.06159420289855073\textwidth}P{0.09547101449275362\textwidth}}
\tabcellsep \tabcellsep \multicolumn{2}{l}{3: Logistic regression result}\tabcellsep \\
\multicolumn{2}{l}{Prb. Of being in child labour}\tabcellsep Cofficient\tabcellsep Std. Err.\tabcellsep z\tabcellsep P > |z|\\
\multicolumn{2}{l}{Sex of parents}\tabcellsep .8604187\tabcellsep 1.298265\tabcellsep 0.66\tabcellsep 0.507\\
\multicolumn{2}{l}{Age of the Households}\tabcellsep .0131227\tabcellsep .0283364\tabcellsep 0.46\tabcellsep 0.643\\
\tabcellsep Family Size\tabcellsep .8290725\tabcellsep .3150063\tabcellsep 2.63\tabcellsep 0.008***\\
\multicolumn{2}{l}{Dependency Ratio}\tabcellsep .7416148\tabcellsep .4888516\tabcellsep 1.52\tabcellsep 0.129\\
\multicolumn{2}{l}{Head Eqations}\tabcellsep -.1986955\tabcellsep .0379914\tabcellsep -5.23\tabcellsep 0.001***\\
\multicolumn{2}{l}{Occupation of parents}\tabcellsep 4.285659\tabcellsep 1.599432\tabcellsep -2.68\tabcellsep 0.007***\\
\multicolumn{2}{l}{Distance from school}\tabcellsep 0067724\tabcellsep .0279624\tabcellsep 0.24\tabcellsep 0.809\\
\multicolumn{2}{l}{Loss of parents}\tabcellsep 3.829672\tabcellsep 2.057537\tabcellsep 1.86\tabcellsep 0.063*\\
\tabcellsep cons\tabcellsep 5.185204\tabcellsep 3.1686\tabcellsep -1.64\tabcellsep 0.102\\
\multicolumn{3}{l}{Number of obs = 94Log likelihood = -19.87}\tabcellsep \tabcellsep \\
\multicolumn{2}{l}{LR chi2(8) = 90.40}\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{2}{l}{Prob > chi2 = 0.0000}\tabcellsep \tabcellsep \tabcellsep \\
Pseudo R2\tabcellsep = 0.6946\tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
  {\small\itshape [Note: NB: * significant at 1\%, ** significant at \% and *** significant at 10\% Source: Survey result using STATA, 2017]} 
\caption{\label{tab_4}Table 3 .}\end{figure}
 			\footnote{Empirical Analysis on Determinants of Childlabor and Associated Problems (The Case of Damot Gale District , Wolaita Zone, SNNPR, Ethiopia) © 2017 Global Journals Inc. (US)} 			\footnote{Year 2017} 		 		\backmatter  			  				\begin{bibitemlist}{1}
\bibitem[Gujarati and Porter ()]{b7}\label{b7} 	 		\textit{Basic Econometrics (Fifth Edition ed.)},  		 			\& Gujarati 		,  		 			Porter 		.  		2009. New York, America: McGraw-Hill/Irwin.  	 
\bibitem[Guarcello ()]{b6}\label{b6} 	 		\textit{Child Labor and Youth Employment: Ethiopia Country Study},  		 			Rosati Guarcello 		.  		2007.  	 	 (Working Paper) 
\bibitem[Etzensperger ()]{b3}\label{b3} 	 		‘Child labor discussion paper’.  		 			Etzensperger 		.  	 	 		\textit{Responsibility Social Investment AG}  		2012.  	 
\bibitem[Basu ()]{b0}\label{b0} 	 		‘Child Labor: Cause, Consequence, and Cure, with Remarks on International Labor Standards’.  		 			K Basu 		.  	 	 		\textit{Journal of Economic Literature}  		1999. 37  (3)  p. .  	 
\bibitem[Child Labour: Social, Employment and Migration Working Papers (2009)]{b13}\label{b13} 	 		\textit{Child Labour: Social, Employment and Migration Working Papers},  		2009. May 2009.  	 
\bibitem[Tesema ()]{b11}\label{b11} 	 		\textit{Contribution of wotershade Manegment for climate Change.The Case Study of Wolayta Zone},  		 			A Tesema 		.  		2015. Addis Ababa,Ethiopia.  	 	 (Master Thesis) 
\bibitem[Greene ()]{b5}\label{b5} 	 		\textit{Econometric Analysis, Fifth Edition},  		 			Greene 		.  		1993. New York; Upper Saddle River,New Jersey: Pearson Education, Inc. p. 7458.  	 
\bibitem[Empirical Analysis on Determinants of Childlabor and Associated Problems (The Case of Damot Gale District]{b14}\label{b14} 	 		\textit{Empirical Analysis on Determinants of Childlabor and Associated Problems (The Case of Damot Gale District},  		Wolaita Zone, SNNPR, Ethiopia.  	 
\bibitem[ILO (International Labour organisation) ()]{b9}\label{b9} 	 		\textit{ILO (International Labour organisation)},  		1993. Geneva.  	 	 (Bulletin of Labour Statistics 1993-3) 
\bibitem[Habtamu ()]{b8}\label{b8} 	 		\textit{Livelihoods And Survival Strategies Among The Migrant Sho -shinning Children: A case study at Arada Sub -City},  		 			Habtamu 		.  		2011. Addis Ababa.  	 	 (Ma Thesis) 
\bibitem[Myers ()]{b10}\label{b10} 	 		\textit{Not just avictam; the child as catalyst and witness of contamperary Africa},  		 			Plateau Myers 		.  		2005.  	 
\bibitem[Csa ()]{b2}\label{b2} 	 		\textit{Population and Housing Census of Ethiopia},  		 			Csa 		.  		2007. Addis Ababa.  	 
\bibitem[Cochran ()]{b1}\label{b1} 	 		\textit{Sampling techniques},  		 			Cochran 		.  		1977. New York: John Wiley\& Son.  	 
\bibitem[The Ethiopian Federal Democratic Republic Constitution ()]{b4}\label{b4} 	 		\textit{The Ethiopian Federal Democratic Republic Constitution},  		1995. Addis Ababa.  		 			Federal Democratic Republic Of Ethiopia ; Berhanena Selam Printing Enterprise 		 	 
\bibitem[World Population Prospects: The 2012 Revision: Key Findings and Advanced Tables UNESAD ()]{b12}\label{b12} 	 		‘World Population Prospects: The 2012 Revision: Key Findings and Advanced Tables’.  		 ESA/WP.227.  	 	 		\textit{UNESAD}  		2013.  	 	 (Working Paper) 
\end{bibitemlist}
 			 		 	 
\end{document}
