Caste-based discrimination has been a feature of Indian society for many centuries, and despite sustained efforts by the Indian government, caste-based inequity still  persists in socio-economic status, educational attainment and in health outcomes. A potential source of caste inequity, which is as yet unexplored, is in the differential development of cognitive skills during early childhood. These skills have been associated with performance in school and even with longer-term employment and wage outcomes.

Child labour in India has long been the focus of research, policy concern and intervention. This paper presents a preliminary analysis of children's work in cottonseed production in Andhra Pradesh, drawing on evidence from two case studies from the qualitative component of Young Lives. In parts of rural Andhra Pradesh, children work in the cotton fields for two to three months of the school year. Children highlighted the importance of this work in their everyday lives and its consequences for their schooling. Evidence shows marked gender and age differentiation.

The NREGS is an ambitious public works program intended to provide a basic safety net to the rural poor in India. Institutionalised in 2005, it is currently the largest public works program in the world. Despite its scale and the political importance attached to the program, its success in targeting and its impact on participating households has not been well studied. This paper attempts to study two aspects of the program's functioning using data from the Young Lives longitudinal Study conducted in Andhra Pradesh.

In this study, I examine the extent to which child labour is explained by the main determinants proposed in the literature. I focus on three determinants: income shocks, household composition and parental preferences. In addition, I examine the role of child-specific characteristics, such as psycho-social abilities and nutritional endowment. While there is extensive literature on child labour determinants, it is predominantly theoretical and tends to treat each determinant in isolation from the others.