Using logistic regression to identify the factors affecting child labor in Red Sea State

Abstract

Sudan, like many countries, suffers from the prevalence of child labor due to the economic conditions it is undergoing in its various states. The study aims to identify the factors affecting child labor in the Red Sea State (Sudan). The study adopted a descriptive-analytical approach and multiple logistic regression. Because of the lack of data and information about child labor in Sudan, the study depended on a questionnaire as a tool for data collection. The study focused on the children in the age group (7-15) years. The sample type was the purposive sample, and the size of the sample was (133) children. Data analyzed using Statistical Package for Solution Services (SPSS). The multiple logistic regression model applied to investigate the relationship between the dependent variable (child labor) and the explanatory variables. The results explained that both males and females entered the labor market, but the number of males who entered the labor market was more than females. Also, the study found that explanatory variables such as age, education of mother, marital status of parents, and the number of family members had significant effects on child labor at a 5% level of significance. However, gender, father job, and parents’ encouragement were found to be statistically insignificant. The study recommended that a good database system must be provided and organize accurate information about child labor in ordered to help policymakers and researchers, and children's education, which may contribute to protecting children from the labor market, must be compulsory.