Modeling the Key Determinant of Child Labour in Kenya

Abstract

Child labour is an e ect of many factors that are addressed in the MDGs, SDGs and verious policy documents. In the listen years , programmes and policies have not been established out to address the issues of child labour owing to the fact that this has not been adequately captured or analysed in national data and statistics. The main objective of this study is to investigate the key determinants of child labour in Kenya. The study focused on children of the aged between 5 and 14 years using the KNBS Household survey Data of 2017. Mixed e ect binary logistic regression was conducted to analyse the data. The explanatory variables are: child age and sex,household size,family head gender, type of household residence, relationship of a child to the household head, household head level of education, hours spent by a child on household chores, average monthly household income and expenditure and area of residence. The model results show that the age of a child, the highest grade attended by the household head (household head education),average household monthly income, hours spent by a child in carrying out household chores and area of residence are important determinants of child labour in Kenya. The ndings indicate that the chance for child to be engaged in work increases with age. Household income has negative in uence on the chance for child labour. Higher level of education of the household head decreases the chance of sending child to work. In addition, increase in hours spent on household chores increases possibility of child labour. Lastly, the type and are of residence signi cantly a ect child labour. Policy interventions to be enhanced for reduction of child labour are improving households living conditions by increasing their average monthly income. Raise adult literacy levels. Reduce hours spent by children in taking household chores and enhance gender equality in education. Address regional disparities in probability of child labour by allocating more educational resources to the devolved government units with high child labour probability.