Temporal Dynamics of Development Aid in Africa: Evidence from a Staggered Difference-in-Differences Study of China and World Bank Projects in Africa

2026-06-04

AI Summary

This study addresses the limitations of traditional aid evaluations, which struggle to disentangle treatment effects from baseline differences and often focus disproportionately on infrastructure projects. Leveraging a balanced panel of 2,166 Demographic and Health Surveys (DHS) clusters across 35 African countries from 2002 to 2013, combined with geocoded Chinese and World Bank aid projects and satellite-based estimates of household wealth, the authors employ the dCdH staggered difference-in-differences estimator to mitigate bias arising from staggered treatment timing—offering a robust alternative to conventional two-way fixed effects (TWFE) models. Event studies and pretreatment diagnostics further enhance identification credibility. Findings reveal significant sectoral heterogeneity: World Bank aid shows pronounced positive effects in health, while Chinese aid is positively associated with water supply and social infrastructure. Effects in the energy sector appear only under TWFE but vanish under dCdH, casting doubt on the general claim that aid consistently boosts local wealth.

Abstract

Subnational studies of aid effectiveness often rely on repeated cross-sections or nighttime lights, making it difficult to separate local treatment effects from baseline differences and potentially favoring infrastructure-heavy projects. We address these limitations by studying World Bank and Chinese development projects in Africa with a balanced panel of 2,166 DHS clusters across 35 countries from 2002 to 2013. Geocoded AidData projects are linked to satellite-imputed International Wealth Index estimates, a household-centered measure of material living standards. We compare a conventional two-way fixed effects (TWFE) event-study with the switcher--stayer estimator of de Chaisemartin and D'Haultfoeuille (dCdH), which avoids contaminated comparisons under staggered treatment timing. Pre-treatment diagnostics show that project placement is frequently selective: clusters that later receive projects often begin from weaker relative positions before treatment onset. Consequently, TWFE often implies larger post-treatment gains than the preferred staggered-treatment design supports. Under dCdH, the evidence becomes more selective and sector-specific. For the World Bank, positive evidence is strongest in Health, while Education shows positive but less cleanly identified gains. For China, Water Supply and Sanitation and Other Social Infrastructure and Services show positive associations with local wealth, although residual selection concerns remain. By contrast, Chinese Energy Generation and Supply appears strongly positive under TWFE but falls close to zero under dCdH. Overall, the results do not support a donor-wide claim that either the World Bank or China uniformly improves local wealth. Instead, estimated effects are concentrated in a limited set of donor--sector panels and depend strongly on how treatment timing, selection, and outcome measurement are handled.

Problem

• aid effectiveness
• subnational analysis
• treatment effect identification
• baseline differences
• development projects

Innovation

• staggered difference-in-differences
• switcher-stayer estimator
• satellite-imputed wealth index
• subnational aid effectiveness
• treatment timing bias