Child labor remains a global challenge with devastating effects on children, particularly in sub-Saharan Africa, the region with the highest prevalence of the practice. Unfortunately, efforts to curb this problem in the region have yielded few results. This study used a binary logistic regression model to examine the effect of child attributes and household socioeconomic characteristics on child labor outcomes in some selected sub-Saharan African countries, using interaction terms to explain the conditional relationship among correlates. To achieve this objective, the study used data from the UNICEF Multiple Indicators Cluster Survey Round 6 (2017–2021) across 10 sub-Saharan African countries: Nigeria, Sierra Leone, Togo, DR Congo, Madagascar, Malawi, Sao Tome and Principe, Ghana, Guinea Bissau, and Chad. The result revealed, contrary to mainstream ideas, that child’s educational attainment up to secondary education increases child labor by 2.2% in the selected sub-Saharan African countries while increase in child’s age increases child labor by 3.5%. Household size was also observed to increase child labor by 0.2% but increasing mother’s education to at least primary level and higher wealth status reduced child labor by 1.1% and 4.7% respectively. Even though the marginal effect of the interactions could not be determined, the logistic regression result revealed that the interaction of age with gender and maternal education with poverty are significant in influencing child labor. This study recommended the promotion of girl-child education, implementation of poverty eradication programs and policies, promotion of population control measures, and the introduction of child protection as a subject of study in schools to drive behavioral and social changes.
Onyechi et al. (2026) studied this question.