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Machine learning methods are increasingly applied in the development policy arena. Among many recent policy applications, machine learning has been used to predict poverty, soil properties, and conflicts. In a recent Policy Research Working Paper by Paolo Brunori, Paul Hufe and Daniel Mahler (BHM hereafter), machine learning methods are utilized to measure a popular understanding of distributional injustice – the amount of unequal opportunities individuals face.
Rising income and wealth inequality have come into sharp focus since the Global Crisis. Using US and European data, this column explores the factors contributing to unfair inequality, focusing on equality of opportunity and freedom from poverty. The results show that unfair inequality is greater in the US than anywhere in Europe, and that it has been increasing over time. The findings also show that relying solely on measures of equality of opportunity will severely underestimate unfair inequality.
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