\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={Scan to know paper details and author's profile},
 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{1970-01-01 (revised: 01 January 1970)}
\def\TheID{\makeatother }
\def\TheDate{1970-01-01}
\title{Scan to know paper details and author's profile}
\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}

             \affil[1]{  }

\renewcommand\Authands{ and }

\date{\small \em Received: 1 January 1970 Accepted: 1 January 1970 Published: 1 January 1970}

\maketitle


\begin{abstract}
        


Guy Armand Onambele & Odette Semevo

\end{abstract}


\keywords{}

\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*}


\begin{textblock*}{10cm}(1.05cm,3cm)
{{\textit{CrossRef DOI of original article:}} \underline{}}
\end{textblock*}\let\tabcellsep& 	 	 		 
\section[{I. INTRODUCTION}]{I. INTRODUCTION}\par
Benin is a country in West Africa with about 12 million inhabitants. Like most countries, Benin is in line with the Sustainable Development Goals. According to the fifth Demographic and health survey data set  {\ref (Benin DHS 5, 2017} {\ref -2018)}, the gross attendance rate in primary school is 89\%, with a gap between girls and boys (94\% for boys and 84\% for girls) and the net rate is 65\%, again with a gender gap to the disadvantage of girls. Thus, it should be noted that efforts still need to be made to achieve full enrollment of children in school. Concerning decision-making within the household, the fifth Demographic and health survey data reveal that in relation with women's health, only 11.5\% of women decide alone, in 34.8\% of cases, the decision is joint (both parents) and in 53.1\% of cases, it is the man alone who decides on behalf of woman. Regarding essential household purchases, 8.8\% of women decide alone; in 38.4\% of cases, the decision is join,t and in 52.1\% of cases, it is the man alone. What about decision-making for child school enrollment? Universal quality education is part of the 17 th Sustainable Development Goals to be reached by 2030. This work examines the relationship between household characteristics and school enrollment of children aged 6-11 in Benin. We are interested in the age group of children likely to be enrolled in primary school. The official age of entry into primary school in Benin is 5 years. But not all 5-year-olds are likely to be enrolled in primary school, so to have statistics of children actually in the education system, we considered 6-year-olds. In addition, primary education consists of six years of study. For these reasons, we considered the 6-11 age group.\par
The general hypothesis underlying this study is that household characteristics influence children's school enrollment. 
\section[{Literature Review}]{Literature Review}\par
Education is a crucial element in the life of every individual in society. According to UNESCO, it is a set of means allowing the development of the physical, moral, and intellectual faculties of a human being. The right to education is unique and necessary because it empowers individuals to exercise all their other rights  {\ref (civic, political, economic, social, or cultural)}, giving them the opportunity to live a dignified life, while ensuring a better future for all. Education is also a means of empowering children and even adolescents to become active participants in the transformation of the societies in which they live  {\ref (INSAE, 2018)}.  . In Togo, for example, more than 60\% of children from wealthy households are enrolled in school, while only 15\% of children from poor households are schoolarised. Children from poor households are engaged in paid work to support their families  {\ref (Vissého Adjiwanou, 2005)}. In studying the effect of household standard of living on school completion in twenty-five countries, Filmer D. and Pritchett L.,  {\ref (1999)} found that in Benin, with data from the 1993 demographic and health survey, the completion gap in primary school is 41.1\%; 39.5\% and 19.3\% respectively for poor, middle and wealthy households. In Burkina Faso, for example, Dramane Boly (2017) found that the standard of living is a determining factor in children's schooling. These studies, therefore show that the household's standard of living has a significant influence on children's school enrollment and completion. Okurut, F.N. and D.O. Yinusa, (2010) also found that in Botswana, children living in female-headed households are more likely to be enrolled in school. 
\section[{Age Range Effect and Social Network}]{Age Range Effect and Social Network}\par
Analyzing household characteristics, Chabi M. and Attanasso O.  {\ref (2015)} found that the presence of children under 6 years old in a household puts pressure on the household's resources, and therefore, influences the schooling of the household's children. This age range (6-11 years) corresponds to the official age for being in primary school. In a situation of limited resources, the presence of this age range puts pressure on the household's resources, and therefore affects the chance that all children to attend school. On the other hand,  {\ref Moussa B. et al (2014)} assessed the combined effect of family networks and sibling size on children's schooling and concluded that there is a positive relationship between an increased number of children and the use of family mutual aid for children's education. This is due to the fact that parents with a high number of children depends on social networks to send their childrens to school more than those with fewer children. 
\section[{Gender and Family Relationship}]{Gender and Family Relationship}\par
Individual characteristics of children are also elements that influence their enrollment. Some authors have shown in their work that being female is negatively associated with the probability of enrollment and attainment of a high level of education. Girls are a cheaper source of labor for agricultural, household, and commercial activities, so mothers cannot do without their contribution. As a result, parents choose to keep girls at home and give priority to boys  {\ref (Lokonon P., 2018)}. Further on, Dramane B.  {\ref (2017)} shows that in Burkina Faso, girls who are not related to the head of the household are less likely to attend school due to their use in domestic work. In Mali, Kuepie M. and Misangumukini N. (2012) found that permanent resource constraints have a more significant impact on girls' schooling than on boys'. Similarly, these authors find that girls' school success is sensitive to the educational capital of the adults in the household.   {\ref (Gouda and Sekher, 2014)} found that the dropout rate among children of illiterate parents is four times higher than among literate parents. Similarly, the likelihood of dropping out is high among children whose parents do not work. In Burkina Faso, for example, research on women's participation in household expenses and women's decision-making role in children's schooling has shown that mothers' education is a determining factor in their children's schooling  {\ref (Madeleine Wayack-Pambé, 2007)}. 
\section[{Decision-Making}]{Decision-Making}\par
The relationship between spouses, particularly the possibility for each of them to participate in household decisions, is an important indicator of gender relations in society and contributes to the well-being of the entire family. Thus, some gender studies have been conducted to determine the factors contributing to women's participation in household decision-making. For example, it is known that women's participation in the modern labor market or paid work, older age, urban residence, type of union, and access to the media are factors that positively influence their participation in decision-making within their households  {\ref (Thionbiano, 2014;} {\ref Ampale, 2015)}. The International Food Policy Research Institute (UNICEF 2007), reveals that if men and women had equal influence in household decision-making, the incidence of underweight children under three years of age in South Asia could be reduced by 13 percent, resulting in 13.4 million fewer malnourished children in the region. Similarly, in sub-Saharan Africa, 1.7 million more children would be adequately nourished based upon gender balancce in decision-making. One would expect this gender equality to be a factor in getting children into school. 
\section[{II. MATERIALS AND METHODS}]{II. MATERIALS AND METHODS} 
\section[{Sampling}]{Sampling}\par
The data used are from the Benin Demographic and Health Survey conducted in 2017-2018. Our base consists of women aged 15-49, men aged 15-64, and children aged 6-11. In total, we have 13,958 children and 4,035 couples.\par
The dependent variable in this study is the school enrollment of children aged 6-11 at the time of the survey. It is a binary variable taking the value 1 when the child is enrolled in school and 0 otherwise.\par
The independent variables are: for the household (standard of living, area of residence, type of union); for the child (sex); for the mother/ caregiver of the child (participation in decision making on health and household priority expenditures, education level, age, current occupation); for the father/guardian of the child (participation in decision-making on health, household priority expenditures, education level, age, current occupation) The "participation in decision-making" variable is a composite. It was designed by considering cases where the woman/man decides alone, jointly with her/his partner and does not participate in the decision on health and priority household expenses. 
\section[{Data Analysis}]{Data Analysis}\par
The analysis has two components: descriptive (univariate, bivariate) and explanatory (binomial logistic regression and prioritization). The bivariate analysis measures the association between the dependent variable and the independent variables and is verified by a Chi2 test at the 5\% threshold.\par
The explanatory component through binomial logistic regression allows us to measure the net effect of each of the independent variables on children's school enrolment. The choice of this analysis model is justified by the qualitative and dichotomous nature of the dependent variable.\par
As for prioritization, it allows us to see the level of contribution of each factor to the explanation of children's school enrolment. The process consists London Journal of Research in Humanities and Social Sciences of taking the difference between the Chi-square of the final model, including all the variables, and the Chi-square obtained from the model without the variable, and relating the result to the final Chi-square to obtain the contribution of the variable in explaining the phenomenon.  
\section[{III. RESULTS AND ANALYSIS}]{III. RESULTS AND ANALYSIS} 
\section[{Women Characteristics}]{Women Characteristics}\par
The majority of women have no education (68.8\%). Respectively 16,6\% and 14,6\% have a primary and secondary level or more. Very few women (4,2\%) decide alone when it comes to their health care and significant household purchases.  
\section[{Men Characteristics}]{Men Characteristics}\par
One in two fathers/guardians has no education (50\%). For those who are schoolarised, 24\% have reached primary school and 26\% secondary school or higher.  
\section[{Bivariate}]{Bivariate}\par
The results of the bi-variate analysis showed that, with the exception of participation in decisionmaking from men's statements and the gender of the head of household, all variables are significantly associated with school enrollment of 6-11-year-olds at the 5\% threshold. 
\section[{Logistic Regression}]{Logistic Regression} 
\section[{Household's characteristics}]{Household's characteristics}\par
The type of union that links parents influences the schooling of children aged 6-11. Children living in a polygamous household are 0.76 times less likely to be enrolled than those in a monogamous household. Household's standard of living was found to be highly significantly associated with London Journal of Research in Humanities and Social Sciences 
\section[{Children's characteristics}]{Children's characteristics}\par
The results show that, at the 5\% level, the gender of the child has a significant association with school enrollment. Thus, the results show that girls are 0.81 times less likely to be enrolled than boys. 
\section[{Mother's Characteristics}]{Mother's Characteristics}\par
Concerning the mother's characteristics, the results show that the woman's involvement in decision-making is significantly associated with children's school enrollment at the 1\% level. Thus, children whose mothers or guardians make decisions alone or jointly are respectively 1.94 and 1.44 times more likely to be enrolled than their counterparts whose mothers or guardians are not involved in decision-making. 
\section[{Parent's education and literacy}]{Parent's education and literacy}\par
At the 1\% level, maternal education is positively associated with children's school enrollment. Thus, children whose mothers or guardians have primary and secondary education or more are 1.64 and 2.68 times more likely to be enrolled than children whose mothers have no education. 
\section[{Parent's Age Range}]{Parent's Age Range}\par
Children whose mothers or janitors are between the ages of 30-39 and 40-49 are, respectively 1.59 times and 1.83 times more likely to be enrolled in school than their counterparts whose mothers are between the ages of 15-29 at the 1\% threshold. 
\section[{Parent's occupation}]{Parent's occupation}\par
In contrast to the previous woman's characteristics, her current occupation is not significantly associated with children's school enrollment. 
\section[{Spouse/Guardian's Characteristics}]{Spouse/Guardian's Characteristics}\par
When analyzing the results according to the characteristics of the spouse or guardian, the results show a negative (non-significant) association between the spouse's participation in decision-making and the children's school enrollment. Like the woman's level of education, the spouse's level of education shows a positive association with children's school enrollment at the 1\% threshold. 
\section[{Father/Guardian's Characteristics}]{Father/Guardian's Characteristics}\par
Children in households where the father/guardian has a primary and secondary level or higher education are respectively 6.12 and 3.40 times more likely to be enrolled than those living in a household where the father/guardian has no education. The results show that the age of the father/guardian and his current occupation are not significantly associated with children's school enrollment. 
\section[{Prioritization of Factors}]{Prioritization of Factors}\par
Table \hyperref[tab_3]{2} below shows factors that influence children's school enrollment, classified according to their importance : the spouse's level of education, the household's standard of living, the woman's level of education, the women's participation in decision-making, the woman's age, the child's sex and the type of union.   
\section[{IV. DISCUSSIONS}]{IV. DISCUSSIONS}\par
This study aimed to analyzing the influence of household characteristics on school enrollment of 6-11-year-olds in Benin. Similar works generated the same findings focusing on the impact factors. 
\section[{Spouse's Level of Education}]{Spouse's Level of Education}\par
About the characteristics of the spouse, the latter's level of education is a factor that is significantly associated with children's schooling. We can thus say that the more educated the parents are, the better they understand the importance of sending their children to school. Mabrooka Altaf, Tusawar Iftikhar Ahmad, Muhammad Azhar Bhatti, (2022) highlighted maternal education as the most influential and decisive factor in enhancing school enrollments of male children and female children in Pakistan. 
\section[{Household Standard of Living and Socio-Cultural Patterns}]{Household Standard of Living and Socio-Cultural Patterns}\par
Similar to studies carried out by  {\ref Adjiwanou (2005)} and Bambara and Wayack-Pambè (2019), we reached findings showing that the household's standard of living is determining children's school enrollment. Thus, children in wealthy households are more likely to be enrolled than those in poor families. These results also confirm the study by  {\ref Filmer and Pritchett (1999)}, who found that in Benin, with data from 1993 Demographic and Health Survey, the primary school completion gap is 41.1\%, 39.5\% and 19.3\%\par
for the poor, middle and rich households respectively. This could be explained by the fact that wealthy households can meet the costs of sending their children to school, which is not always the case for poor families. In the Beninese context, the low enrollment rate of girls in school could be explained by the fact that girls constitute an essential workforce for mothers. They help their mothers on the one hand to take care of their younger brothers, on the other hand, to help mothers in their activities. In a socio-cultural context, parents consider the girl as the property of another person from the moment she gets married. So sending her to school would be an additional expense for them with no benefits.\par
In addition, using data from the 1968-2013 October Current Population Survey to document trends in 3-and 4-year-old children's enrollment in center-based early childhood education, Katherine Magnuson and Jane Waldfogel (  {\ref 2016}) have tried to focus on gaps in enrollment among children from low-, middle-, and high-income families. They found that income-related gaps in enrollment widened in the 1970s and 1980s but appear to have plateaued or narrowed for succeeding cohorts. These patterns are consistent with recent trends in income-related gaps in school achievement. 
\section[{Woman's Level of Education}]{Woman's Level of Education}\par
The level of education of mothers was found to be significantly associated with children's schooling.   {\ref Han, 2021;} {\ref Gouda and Sekher, 2014)}, which led to the results that the dropout rate among children of illiterate parents is four times higher than that of literate parents.\par
Throughout Ghana Living Standard Survey round 6 (GLSS 6) data, Abdul Malik Iddrisu, Michael Danquah and Peter Quartey (2017) demonstrated that parental education, household income and the gender of the head of the household are significant factors in households' children's schooling decisions. Educated parents are more likely to enroll their children in primary school and keep them until they complete primary education. The authors observed that educated parents do not promote a gender-biased investment in the schooling of children at the primary level. In addition, household welfare has a positive impact on children's completion of primary school. 
\section[{Women's Participation in Decision-Making}]{Women's Participation in Decision-Making}\par
Concerning factors related to parents, the results showed that women's participation in household decision-making is a factor positively associated with children's schooling. We can therefore affirm that a more egalitarian gender relations within couples that gives women the right to intervene in decision-making is, in turn, a factor that promotes children's education. Thus, women have a positive influence on children's education when they intervene in household decisions. 
\section[{Age of the Woman}]{Age of the Woman}\par
An advanced age of mothers is a favorable factor for the schooling of children. We can thus say that an advanced age confers more maturity, and respect to women and consequently, they can positively influence the education of their children. At slightly older generations, women become aware of the importance of sending their children to school. 
\section[{Sex of the Child}]{Sex of the Child}\par
The results showed that girls are more likely to be less educated than boys. Thus, the female gender is negatively associated with being in school. This reality was revealed by the DHS-V data  {\ref (2017)} {\ref (2018)}. According to this survey, despite all the policies put in place by the authorities to encourage the enrollment of girls in school, there is a gap (10 percentage points) to be filled between boys' gross enrollment rate (94\%) and girl's gross enrollment rate (84\%). Uzma Naz, Zainab Ejaz and Naveed Khan (2019) analyzed major factors responsible for high dropouts in rural areas in Islamabad (Pakistan). Besides the distance from school to home, financial constraints is the most crucial reason for dropping out. Moreover, the education of the father, the age of the child, and the gender of the child are also highly significant variables that determine the probability of a child dropout. 
\section[{Type of Union}]{Type of Union}\par
The results of the explanatory analysis showed that the type of union in which a child lives influences their schooling to the extent that a child in a polygamous household is likely not to be enrolled in school. This could be explained by the fact that in a polygamous family there are several children, which increases the burden of schooling on parents, so they tend to enroll some children at the expense of others. This result is similar to work done by Attanasso M.O. and Chabi M. (2015), who found that the number of children under the age of 5 and the number of children in the 6-11 age group are variables that negatively affect school enrollment or level. That implies in a polygamous household, there would be many children in these age groups. 
\section[{V. CONCLUSIONS}]{V. CONCLUSIONS}\par
The purpose of this study was to examine the influence of household characteristics on the school enrollment of 6-11-year-olds. After presenting the context of the study, and the empirical work done by other researchers, we found that several factors directly influence children's school enrollment. The data used in this study came from the fifth Benin Demographic and Health Survey  {\ref (DHSB 5, 2017} {\ref (DHSB 5, -2018))}. Our target population was women aged 15-49 and men aged 15-64 who were in a union or living with a partner at the time of the survey, as well as children aged 6-11 living in households. Both descriptive and explanatory methods of analysis were used in this study.\par
The descriptive analysis method allowed us to test the relationship between children's school enrollment and all the variables in the study at the bivariate level. An explanatory analysis based on binomial logistic regression allowed us to determine the factors that, in addition to the joint participation of couples in household decisions, influence children's schooling.\par
We found that the spouse's level of education, the household's standard of living, the woman's level of education, the women's participation in household decision-making, the woman's age, the child's sex, and the type of union are the factors that influence children's school enrollment in order of importance.\par
Despite the findings, this study does not claim to have covered all aspects of couples' joint participation in household decisions and all factors that might influence children's school enrollment. Thus, it should be noted that this study has some limitations. Not all variables related to household decision-making were included. It should also be noted that there is no specific question in the database on decision-making regarding children's education. However, this could more easily help to identify the parent who contributes most through their decision to the enrollment of children in the household. Nevertheless, these limitations do not alterate the results of our study. This study is very important for drawing a streamline to design better policy actions in favor of children's schooling.\par
Given the findings of our study, we recommend that schooling policies be perpetuated. Because today's children will be tomorrow's parents, the education of their offspring will be impacted by their litaracy or education level. It's also recommended to carry out more policies to strengthen the economic capacities of households.      
\section[{ANNEXES}]{ANNEXES}\begin{figure}[htbp]
\noindent\textbf{1}\includegraphics[]{image-2.png}
\caption{\label{fig_0}Figure 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-3.png}
\caption{\label{fig_1}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4}\includegraphics[]{image-4.png}
\caption{\label{fig_2}Figure 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.8280409041980624\textwidth}P{0.00457481162540366\textwidth}P{0.006404736275565124\textwidth}P{0.002744886975242196\textwidth}P{0.008234660925726587\textwidth}}
\multicolumn{5}{l}{Despite the importance of education and the}\\
\multicolumn{5}{l}{policies put in place by governments, we note that}\\
\multicolumn{5}{l}{not all children are enrolled in school, and}\\
\multicolumn{5}{l}{inequalities are observed across countries, and}\\
\multicolumn{5}{l}{within each country. Several factors certainly}\\
\multicolumn{5}{l}{describe this observation. There has been}\\
\multicolumn{5}{l}{considerable discussion of the factors that explain}\\
\multicolumn{5}{l}{children's schooling, as well as the factors that}\\
\multicolumn{5}{l}{influence women's participation in household}\\
\multicolumn{5}{l}{decision-making. But, to date, relatively little}\\
\multicolumn{5}{l}{work has examined the influence of couples' joint}\\
\multicolumn{5}{l}{participation in household decision making on}\\
\multicolumn{5}{l}{children's schooling. Empirical studies that}\\
\multicolumn{5}{l}{determine the factors influencing children's}\\
\multicolumn{5}{l}{education have yielded multiple and diverse}\\
\multicolumn{4}{l}{results depending on the context.}\tabcellsep \\
\multicolumn{3}{l}{1.1.1 Impact of Living Standards}\tabcellsep \tabcellsep \\
\multicolumn{5}{l}{Studies in the West African sub-region have}\\
\multicolumn{5}{l}{shown that in wealthy and female-headed}\\
\multicolumn{5}{l}{households, children are more likely to attend}\\
school\tabcellsep (Alis\tabcellsep Bambara\tabcellsep and\tabcellsep Madeleine\\
\multicolumn{3}{l}{Wayack-Pambè, 2019}\tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_0}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{P{0.3685314685314685\textwidth}P{0.11293706293706293\textwidth}P{0.14067599067599068\textwidth}P{0.15058275058275059\textwidth}P{0.07727272727272727\textwidth}}
\tabcellsep \tabcellsep Chi-square\tabcellsep \tabcellsep \\
Explanatory variables\tabcellsep Final\tabcellsep without the\tabcellsep Contribution\tabcellsep Rank\\
\tabcellsep chi-square\tabcellsep \tabcellsep (\%)\tabcellsep \\
\tabcellsep \tabcellsep variable\tabcellsep \tabcellsep \\
Spouse's level of education\tabcellsep 759,20\tabcellsep 702,22\tabcellsep 11,13\tabcellsep 1 ier\\
Household standard of living\tabcellsep 790,20\tabcellsep 709,20\tabcellsep 10,25\tabcellsep 2 ème\\
Woman's level of education\tabcellsep 759,20\tabcellsep 772,05\tabcellsep 2,29\tabcellsep 3 ème\\
Women's participation in\tabcellsep 790,20\tabcellsep 773,59\tabcellsep 2,10\tabcellsep 4 ème\\
decision-making\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Age of the woman\tabcellsep 759,20\tabcellsep 775,54\tabcellsep 1,85\tabcellsep 5 ème\\
Sex of the child\tabcellsep 759,20\tabcellsep 785,59\tabcellsep 0,58\tabcellsep 6 ème\\
Type of union\tabcellsep 790,20\tabcellsep 789,19\tabcellsep 0,12\tabcellsep 7 ème\\
\tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{Source: Benin DHS 5, 2017-2018}\end{longtable} \par
 
\caption{\label{tab_3}Table 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.244331983805668\textwidth}P{0.26497975708502025\textwidth}P{0.2168016194331984\textwidth}P{0.12388663967611335\textwidth}}
\tabcellsep Terms and\tabcellsep \tabcellsep \\
Variable\tabcellsep \tabcellsep Workforce\tabcellsep Percentage\\
\tabcellsep conditions\tabcellsep \tabcellsep \\
\tabcellsep Boy\tabcellsep 7093\tabcellsep 50,8\\
Gender of children\tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep Girl\tabcellsep 6860\tabcellsep 49,2\\
\tabcellsep Non-registered\tabcellsep 4453\tabcellsep 31,9\\
School registration\tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep Registered\tabcellsep 9500\tabcellsep 68,1\\
School attendance\tabcellsep Boys enrolled\tabcellsep 5058\tabcellsep 53,24\\
by gender\tabcellsep Girls enrolled\tabcellsep 4442\tabcellsep 46,76\\
\tabcellsep \tabcellsep \multicolumn{2}{l}{Source: Benin DHS 5, 2017-2018}\end{longtable} \par
 
\caption{\label{tab_5}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{P{0.30104166666666665\textwidth}P{0.24791666666666667\textwidth}P{0.23906249999999998\textwidth}P{0.06197916666666667\textwidth}}
\tabcellsep Terms and\tabcellsep \tabcellsep \\
Variable\tabcellsep \tabcellsep \multicolumn{2}{l}{Workforce Percentage}\\
\tabcellsep conditions\tabcellsep \tabcellsep \\
Gender of head of\tabcellsep Male\tabcellsep 3895\tabcellsep 96,5\\
household\tabcellsep Woman\tabcellsep 140\tabcellsep 3,5\end{longtable} \par
 
\begin{quote}
London Journal of Research in Humanities and Social Sciences 10 Volume 23 | Issue 5 | Compilation 1.0 © 2023 London Journals Press School Enrollment Factors for Children Aged 6 to 11 in Benin\end{quote}

\caption{\label{tab_6}Table 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{P{0.2176328502415459\textwidth}P{0.3818840579710145\textwidth}P{0.11908212560386475\textwidth}P{0.13140096618357486\textwidth}}
Variable\tabcellsep Terms and conditions\tabcellsep Workforce\tabcellsep Percentage\\
\tabcellsep Decides alone\tabcellsep 171\tabcellsep 4,2\\
Participation in decision\tabcellsep Jointly\tabcellsep 1893\tabcellsep 46,9\\
making\tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep Other\tabcellsep 1971\tabcellsep 48,8\\
\tabcellsep No level\tabcellsep 2775\tabcellsep 68,8\\
Woman's level of education\tabcellsep Primary\tabcellsep 669\tabcellsep 16,6\\
\tabcellsep Secondary or higher\tabcellsep 591\tabcellsep 14,6\\
\tabcellsep Inactive\tabcellsep 645\tabcellsep 16,2\\
\tabcellsep Executive/administration\tabcellsep 126\tabcellsep 3,2\\
Woman's current\tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep Shopkeeper\tabcellsep 1280\tabcellsep 32,2\\
occupation\tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep Farmer\tabcellsep 1052\tabcellsep 26,5\\
\tabcellsep Worker/service\tabcellsep 870\tabcellsep 21,9\\
\tabcellsep 15-29 years old\tabcellsep 1942\tabcellsep 48,1\\
Age of the woman\tabcellsep 30-39 years old\tabcellsep 1376\tabcellsep 34,1\\
\tabcellsep 40-49 years old\tabcellsep 717\tabcellsep 17,8\end{longtable} \par
 
\begin{quote}
\par
Source:Benin DHS 5, 2017-2018    \end{quote}

\caption{\label{tab_7}Table 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.275974025974026\textwidth}P{0.3569264069264069\textwidth}P{0.08463203463203463\textwidth}P{0.13246753246753246\textwidth}}
Variable\tabcellsep \multicolumn{2}{l}{Terms and conditions Workforce}\tabcellsep Percentage\\
\tabcellsep Decides alone\tabcellsep 2231\tabcellsep 55,3\\
Decision making\tabcellsep Jointly\tabcellsep 1744\tabcellsep 43,2\\
\tabcellsep Other\tabcellsep 60\tabcellsep 1,5\\
\tabcellsep No level\tabcellsep 2029\tabcellsep 50,3\\
Spouse's level of\tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep Primary\tabcellsep 952\tabcellsep 23,6\\
education\tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep Secondary or higher\tabcellsep 1054\tabcellsep 26,1\\
Spouse's current\tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep Inactive\tabcellsep 45\tabcellsep 1,1\\
occupation\tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\begin{quote}
© 2023 London Journals Press Volume 23 | Issue 5 | Compilation 1.0\end{quote}

\caption{\label{tab_8}Table 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{5} \par 
\begin{longtable}{P{0.6566523605150214\textwidth}P{0.19334763948497852\textwidth}}
VARIABLES AND MODALITY\tabcellsep NET EFFECT\\
PARTICIPATION OF THE WOMAN IN DECISION\tabcellsep \\
\tabcellsep ***\\
MAKING (statement from the woman)\tabcellsep \\
Only\tabcellsep 1,94***\\
Jointly\tabcellsep 1,44***\\
someone else\tabcellsep Ref\\
SPOUSE PARTICIPATION IN DECISION-MAKING\tabcellsep \\
\tabcellsep ns\\
(declaration from the spouse)\tabcellsep \\
Only\tabcellsep Ref\\
Jointly\tabcellsep 0.84ns\\
someone else\tabcellsep 1.15 ns\\
RESIDENCE ENVIRONMENT\tabcellsep ***\\
Urban\tabcellsep 1.20 ns\\
Rural\tabcellsep Ref\\
GENDER OF THE HEAD OF HOUSEHOLD\tabcellsep Ns\\
Male\tabcellsep Ref\\
Woman\tabcellsep 1,66\\
Type of union\tabcellsep ***\\
Monogame\tabcellsep Ref\\
Polygamist\tabcellsep 0,76*\\
LEVEL OF LIVING\tabcellsep ***\\
Poor\tabcellsep Ref\\
Medium\tabcellsep 1,86***\\
Rich\tabcellsep 3,72***\\
GENDER OF THE CHILD\tabcellsep **\\
Boy\tabcellsep Ref\end{longtable} \par
 
\begin{quote}
London Journal of Research in Humanities and Social Sciences 12 Volume 23 | Issue 5 | Compilation 1.0 © 2023 London Journals Press School Enrollment Factors for Children Aged 6 to 11 in Benin\end{quote}

\caption{\label{tab_9}Table 5 :}\end{figure}
 			\label{foot_0}\footnote{\label{foot_0} Volume 23 | Issue 5 | Compilation 1.0 © 2023 London Journals Press School Enrollment Factors for Children Aged 6 to 11 in Benin} 			\label{foot_1}\footnote{\label{foot_1} Volume 23 | Issue} 			\label{foot_2}\footnote{\label{foot_2} | Compilation 1.0 © 2023 London Journals Press School Enrollment Factors for Children Aged} 			\label{foot_3}\footnote{\label{foot_3} to 11 in Benin} 		 		\backmatter   			 
\subsection[{ACKNOWLEDGEMENT}]{ACKNOWLEDGEMENT}\par
We thank all those who have contributed to the improvement of the quality of this paper. Special thanks to all reviewers. 
\subsection[{BIBLIOGRAPHY}]{BIBLIOGRAPHY}\par
1. IDDRISU A. M., DANQUAH M., QUARTEY P. (2017), ''Analysis of School Enrollment in Ghana : A Sequential Approach'' in Review of Development Economics, 21(4), 1158-1177, 2017 2. ADJIWANOU, V. (2005), "Impact de la pauvreté sur la scolarisation et le travail des enfants de 6-14 ans au Togo", Centre d'Etude et de Recherche sur le Développement International (CERDI) and the Unité de Recherche Démographique (URD), University of Lomé. 3. ALIS, B. and, WAYACK-PAMBE M. (2019), "What effect does the standard of living indicator have on differences in schooling between poor female-headed and maleheaded households in Burkina Faso?" Paper, Institut Supérieur des Sciences de la Population, Université Ouaga Joseph Ki-Zerbo 4. AMPALE E. (2015), Status of women and household decision-making in Central Africa: the case of Congo, Cameroon and Gabon, 17p (Paper at the 7th African Population Conference, South Africa 2015). 5. CHABI M. O. and ATTANASSO M. O. (2005), "Déterminants de la Scolarisation et du Niveau Scolaire en Milieu Rural: Une Etude Empirique au Bénin en Afrique de l'Ouest", Institute for Empirical Research in Political Economy, International Journal of Innovation and Applied Studies. 6. DEON, F. and LANT P. (1999), "The effect of Household Wealth on Educational Attainment: Evidence from 35 Countries," Population and Development Review 7. DRAMANE, B. (2017), "Inégalité scolaire au primaire à Ouagadougou dans les années 2000," Demography, Sorbonne University Paris. NNT: 2017USPCB176. 8. INSAE (2018), Rapport Enquête Démographique et de Santé du Bénin de 2017-2018, 629p. 9. INSAE (2018), Rapport de final de l'Enquête Régionale Intégrée sur l'Emploi et le Secteur Informel (ERI-ESI), Benin 2018, 306 p. 10. KUEPIE M. AND MISANGUMUKINI N. (2012), "Environnement économique et éducatif des ménages et difficultés scolaire des enfants au Mali", Revue d'Analyse Economique, Volume 88, Number 4. https://id.erudit.org/iderudit/1023796ar 11. LOKONON P. (2018), Les facteurs explicatifs de la persistance de la faible scolarisation des filles ans les départements du Mono et du Couffo au Bénin, Thèse de doctorat en Sociologie du Développement, Université d'Abomey-Calavi, 313p. 12. MABROOKA A., TUSAWAR I.A., MUHAMMAD A.B., (2022) ''Maternal Employment, Parental Education Levels and Household's Income : Differential Impacts on the Schooling of Male and Female Children in Pakistan'', in Annals of Social Sciences and Perspective, Volume 3, Number 1, January-June 2022, Pages 235-249 Accessible in http://assap.wum.edu.pk/index.php/ojs 13. MAGNUSON K., WALDFOGEL J. (2016), ''Trends in Income-Related Gaps in 			   			 
\subsection[{Data Availability Statement}]{Data Availability Statement}\par
The data used in this paper is fully available and can be accessed upon request. 			   			 
\subsection[{Funding}]{Funding}\par
The writing of this paper has not been funded or sponsored. It was done at the author's expense. 			  			 
\subsection[{Conflict of Interest Statement}]{Conflict of Interest Statement}\par
The authors state that there is no conflict of interest. 
\subsection[{Ethical Approval}]{Ethical Approval}\par
The data used for the estimates do not include confidential information about individuals or animals that may raise ethical concerns. 
\subsection[{Consent for Publication}]{Consent for Publication}\par
The authors grant his consent for publication of this paper. 			 			  				\begin{bibitemlist}{1}

\end{bibitemlist}
 			 		 	 
\end{document}
