A Comparative Analysis of Wealth Index Predictions in Africa between three Multi-Source Inference Models

Abstract

Poverty map inference is a critical area of research, with growing interest in both traditional and modern techniques, ranging from regression models to convolutional neural networks applied to tabular data, images, and networks. Despite extensive focus on the validation of training phases, the scrutiny of final predictions remains limited. Here, we compare the Relative Wealth Index (RWI) inferred by Chi et al. (2021) with the International Wealth Index (IWI) inferred by Lee and Braithwaite (2022) and Esp\'in-Noboa et al. (2023) across six Sub-Saharan African countries. Our analysis focuses on identifying trends and discrepancies in wealth predictions over time. Our results show that the predictions by Chi et al. and Esp\'in-Noboa et al. align with general GDP trends, with differences expected due to the distinct time-frames of the training sets. However, predictions by Lee and Braithwaite diverge significantly, indicating potential issues with the validity of the model. These discrepancies highlight the need for policymakers and stakeholders in Africa to rigorously audit models that predict wealth, especially those used for decision-making on the ground. These and other techniques require continuous verification and refinement to enhance their reliability and ensure that poverty alleviation strategies are well-founded.