Chinese vs. World Bank development projects: Insights from earth observation and computer vision on wealth gains in Africa, 2002–2013

Debates about whether development projects improve living conditions persist, partly because observational estimates can be biased by incomplete adjustment and because reliable outcome data are scarce at the neighborhood level. We address both issues in a continent-scale, sector-specific evaluation of Chinese and World Bank projects across 9899 neighborhoods in 36 African countries (2002-2013), representative of ∼ 88% of the population. First, we use a recent dataset that measures living conditions with a machine-learned wealth index derived from contemporaneous satellite imagery, yielding a consistent panel of 6.7 km square mosaics. Second, to strengthen identification, we proxy officials’ map-based placement criteria using pre-treatment daytime satellite images and fuse these with tabular covariates to estimate funder- and sector-specific ATEs via inverse-probability weighting. Incorporating imagery often shrinks effects relative to tabular-only models. On average, both donors raise wealth, with larger and more consistent gains for China; sector extremes in our sample include Trade and Tourism (330) for the World Bank (+12.29 IWI points), and Emergency Response (700) for China (+15.15). Assignment-mechanism analyses also show World Bank placement is often more predictable from imagery alone (as well as from tabular covariates). This suggests that Chinese project placements are more driven by non-visible, political, or event-driven factors than World Bank placements. To probe residual concerns about selection on observables, we also estimate within-neighborhood (unit) fixed-effects models at a spatial resolution about 67 times finer than prior fixed-effects analyses, leveraging the computer-vision-imputed IWI panels; these deliver smaller but, for Chinese projects, directionally consistent effects. Methodologically, we extend recent EO–ML causal inference frameworks by fusing pre-treatment satellite imagery with tabular covariates to estimate treatment propensities, and by systematically benchmarking image-augmented estimators against tabular-only and unit fixed-effects designs using new assignment-mechanism diagnostics. Empirically, we provide a continent-wide, sector-specific comparison of the neighborhood-level wealth effects of Chinese and World Bank projects across 9899 African neighborhoods. • Satellite imagery controls reveal that prior studies likely overstated aid benefits in Africa. • On average, Chinese development projects generate larger wealth gains than World Bank projects. • Earth Observation and machine learning improve causal estimates by proxying for planners’ maps. • China’s project placement is less predictable, suggesting unique political or event-driven logics. • Both donors raise wealth, but impacts vary widely by sector, with Emergency Response having the largest effect.