and savings among talented individuals by directly reflecting reward to effort, thus stimulating growth (Ferreira 2005, Marrero et al. 2013). Empirically, the strong negative relationship between IOp and growth has been documented in various settings (Marrero et al. 2013, Bradbury et al. 2016; Carranza 2020).<sup>3</sup> Therefore, singling out the extent and sources of IOp and adequately addressing it is key to fostering much needed economic growth and poverty reduction, especially in SSA where poverty and inequality are high and economic growth is slowing (Wu et al. forthcoming).

The measurement of IOp revolves around the extent to which disparities in outcomes are due to circumstances beyond a person’s control. This typically involves using income or consumption data as the outcome variable and birth characteristics as circumstances, such as region of birth or parental education and occupational background. Because of the heavy data requirements, empirical work has compiled estimates of IOp across many upper-income countries, while estimates in low- and middle-income settings, especially in SSA, are rare.

Until now, the main effort for the SSA region had been Brunori et al. (2019), which estimates IOp across 10 countries using data from 2000-2013. They find that IOp in SSA explains on average 47 percent of inequality in consumption with estimates ranging between 40-56 percent across countries. Though reflecting the data availability and methodological advancements available at the time, the analysis is limited to 10 countries and only a subset of variables is available in each country.<sup>4</sup> Because it is not feasible to observe all characteristics that are fixed at birth and outside an individual’s control, IOp estimates are often seen as a lower bound estimate of true IOp (Ferreira et al. 2011). As often pointed out, when the set of observed circumstances is limited, the degree of underestimation is large and lower bound estimates of IOp end up being of limited use for policy (Kanbur & Wagstaff 2014).

3 The negative relationship between IOp and growth has been documented using variation over time and across regions of the U.S. (Marrero et al. 2013, Bradbury et al. 2016) and in Europe (Carranza 2020). Evidence including developing countries is less conclusive and constrained by data limitations (Ferreira et al. 2018). 4 In another noteworthy attempt, Ferreira et al. (2018) estimate IOp across 75 countries between 1980-2005, including 22 in Sub-Saharan Africa using data from the Demographic and Health Surveys (DHS). However, there are two large disadvantages to using the DHS to estimate IOp: welfare is measured using a wealth index because household consumption is not available, and parental information such as parents’ education is unavailable. Their IOp estimates in SSA range from 1-39 percent, though excluding parental information likely leads to substantial downward bias. A World Bank report (2022a) implemented an IOp analysis in 5 countries in Southern Africa, though data limitations meant it was also unable to consider parental characteristics. This report found that IOp ranged from 15-26 percent, and increased to 46 percent in South Africa after race was included as a circumstance. Other studies examine drivers of inequality more generally – for example, Tetteh-Baah et al. (2024) use the DHS to calculate horizontal inequality in various outcomes across Africa based on location, ethnicity, gender, and religion – but they often do not include income or consumption as an outcome or incorporate information on parental characteristics.

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