Modeling the Key Determinant of Child Labour in Kenya
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Date
2020Author
Thiong'o, Joseph, M
Type
ThesisLanguage
enMetadata
Show full item recordAbstract
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.
Publisher
University of Nairobi
Rights
Attribution-NonCommercial-NoDerivs 3.0 United StatesUsage Rights
http://creativecommons.org/licenses/by-nc-nd/3.0/us/Collections
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