Measuring wealth in rural and urban Africa: findings and recommendations from a multi-country study

Wealth indices enable socioeconomic status measurement, particularly in low- and middle-income countries. However, their reliability, validity, and data collection and computation efficiency can be improved. This analysis aimed to generate four wealth indices in five geographical settings (rural and urban Gambia, rural and urban South Africa, and urban Zimbabwe), to compare and validate these indices and formulate recommendations for wealth measurement in Africa. Population-based cross-sectional data from 5,296 adults aged [≥]40 years were analysed. Principal Component Analyses generated four wealth indices based on 18 variables quantifying house ownership, wall and roofing material, and asset ownership: (1) geographical site-specific, (2) national-level, (3) national composite, and (4) international wealth indices. Recommendations were based on reflective workshops (totalling 6 hours) held with experienced in-country fieldwork coordinators. A very strong correlation (>0.9) between the national-level, national composite and international indices was seen, with strong correlations (>0.6) between these three indices and the site-specific index. In all sites, the international wealth index was associated with greater asset ownership in 44.4% (8/18) of the variables intended to measure wealth i.e., refrigerator, television, working car/truck, tap in the house, flush toilet, decoder/satellite dish, computer, and tiled floors, albeit with rural-urban and country differences. The international wealth index had a moderate prediction (>0.55) across all the other SES measures: higher household income, greater educational attainment, and food security. Reflective workshops established that the accuracy of wealth measurement for multi-country and multi-site studies, using asset-based indices, needs an unambiguous, differential, and clearly defined asset list. Furthermore, consulting local teams to select these assets can reduce data collection burden while increasing index validity, including contemporary asset assessment, e.g., Wi-Fi internet connection. Finally, it is recommended that generated wealth indices be routinely internally validated against individual asset ownership and other socioeconomic measures such as educational attainment.