Economic inequality is a pervasive phenomenon that has long been a feature of both developed and developing countries. Such inequality is particularly problematic in developing countries because of its potential to exacerbate already established and yet detrimental features that characterise most developing countries, such as high unemployment, a large informal sector, and high population growth rates. This thesis investigates how inequality manifests itself through differential student outcomes and also attempts to examine whether or not technological growth destroys jobs and worsens wage inequality. Chapter 2 investigates the relationship between student performance and socio-economic status (SES) among grade six students in Malawi and Namibia. Malawi was specially included in this cross-country comparison because it is likely that the underlying mechanisms that govern the typical/expected SES-performance relationship do not hold in countries as poor as Malawi as they would in more advanced developing countries like Namibia. Using OLS regressions and hierarchical (multilevel) models, the results show an approximately flat socio-economic gradient for student education performance for Malawi, for both the full sample and the reduced samples (urban and rural). In Namibia, in contrast, SES appears to be correlated with student performance. However, this is primarily driven by students who live in urban areas, whereas, like Malawi, rural Namibia also has an approximately flat socio-economic gradient. Chapter 3 builds on this by taking special interest in research in low-income countries like Malawi and the challenge that arises when, in the absence of income/expenditure information, one has to rely on an asset index to distinguish among individuals of comparable SES levels. This follows on discussions in the literature that have well articulated the difficulty asset-based measures have of doing so especially among various shades of poor individuals, that is, differentiating the poor from the very poor: a feature which a measure used in very poor countries like Malawi should have. Chapter 3 explores the use of finite mixture modelling as an alternative approach to achieving this goal. The findings suggest that using this approach makes it possible to distinguish between individuals’ relative SES level in a meaningful way. Lastly, Chapter 4 is primarily interested in examining if technological growth in South Africa contributed to exacerbation of wage inequality and job loss during the period from 1997 to 2015. This analysis is done through the lens of a routine-biased technological change framework whose main hypothesis is that recent technological advancements are biased towards replacing labour in routine tasks. This chapter presents findings from descriptive analysis, OLS regressions, as well as a non-linear systems estimator applied to a normalised CES production function. The results show both descriptive and empirical evidence of a hollowing out of middle-skilled work (done by workers whose occupations typically involve a high share of routine tasks). Further, these findings are differentiated by gender and race.