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 9,899 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.

翻译：关于发展项目能否改善生活条件的争论持续存在，部分原因是观测性估计可能因调整不完整而产生偏差，且社区层面的可靠结果数据稀缺。本研究通过针对36个非洲国家（2002-2013年）9,899个社区（覆盖约88%人口）的大陆规模、分部门评估，同时解决了这两个问题。首先，我们采用最新数据集，通过同期卫星影像衍生的机器学习财富指数衡量生活条件，构建了6.7平方公里网格的连续面板数据。其次，为强化因果识别，我们利用处理前的日间卫星影像代理官员基于地图的选址标准，并将其与表格协变量融合，通过逆概率加权估计资助方与部门特定的平均处理效应。引入影像数据常使效应估计值较纯表格模型收缩。平均而言，两家资助方均提升财富水平，其中中国项目的提升幅度更大且更稳定；样本中的部门极端案例包括世界银行的贸易与旅游业项目（330个，+12.29 IWI点）和中国的应急响应项目（700个，+15.15 IWI点）。分配机制分析还显示，仅凭影像数据（以及表格协变量）往往能更准确预测世界银行项目的选址。这表明中国项目的选址比世界银行项目更受非可见因素、政治或事件驱动因素影响。为探究可观测变量选择的残余问题，我们利用计算机视觉推算的IWI面板数据，在空间分辨率较先前固定效应分析精细约67倍的尺度上，估计了社区内（单元）固定效应模型；这些模型得出更小但方向一致的效应估计值（对中国项目而言）。