Using Logistic Regression Model to Identify Key Socio –Demographic Variables of Child Labour Vulnerability in Ghana
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Date
2013
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Abstract
This study examined the Ghana Child Labour Survey (2003) data with particular interest in children aged 5-17 years, since children within this age group are the vulnerable.
The objectives of the study were to (i) identify key socio-demographic variables among the speculated ones that can contribute significantly to child labour in Ghana. (ii) Examine how these variables vary across the ten regions of Ghana. (iii) To suggest solutions to child labour in Ghana based on the findings.
Logistic regression model was considered an appropriate statistical tool for analysing the data since the selected variables were both categorical and continuous in nature. With child labour (that is work or no work) as the dependent variable and a set of socio-demographic variables as explanatory variables.
Analysis of the data using the logistic regression model revealed that Age of child, literacy of head of household and Residence made strong significant statistical contribution to child labour in all the ten regions of Ghana. The results showed further that in exception of three variables (sex of child, marital status of parents and relationship of child to head of household) that made insignificant contribution to child labour all the variables made significant statistical contribution to child labour in Ghana. This showed that 70% of the selected variables made significant statistical contribution to child labour whilst 30% of the selected variables made no statistical contribution to child labour in Ghana. It was also discovered that only three out of the ten regions namely Greater Accra, Ashanti and Upper East recorded low levels of child labour when the analysis was performed combining all the ten regions of Ghana.
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A thesis submitted to the Department of Mathematics,
Kwame Nkrumah University of Science and Technology,
In partial fulfillment for the requirement for the degree of Master of Philosophy