Integrating Survey and Geospatial Data for Geographical Targeting of the Poor and Vulnerable: Evidence from Malawi
研究提出一种低成本策略，结合家庭消费调查、地理空间数据和部分登记册，通过机器学习模型预测村庄福利水平，在马拉维10个贫困县的450个村庄中验证，模型排名相关性达0.75，优于其他方法。
Abstract To address the challenge of identifying the poorest villages in developing countries, this study introduces a cost-effective strategy that leverages a combination of household consumption surveys, geospatial data, and a partial registry. The study simulates a partial registry, containing data from 450 villages across 10 impoverished districts of Malawi, and contains proxy poverty indicators. These indicators are used to impute estimates of household per capita consumption, which in turn are used to train a prediction model using publicly available geospatial data. This method is evaluated against an imputed reference of village welfare, derived from the 2016 household survey. The partial registry approach is benchmarked against three alternatives: proxy means test scores, the Meta Relative Wealth Index, and predictions from household surveys with geospatial indicators. Results show the partial registry model's rank correlation with actual welfare measures at 0.75, outperforming the other methods significantly, which ranged from −0.02 to 0.2. These findings hold under various robustness checks, including the addition of Gaussian noise, indicating that collecting household-level proxy poverty data in low-income areas can significantly improve the performance of machine learning models that integrate survey and satellite imagery data for village-level geographic targeting.