Integrating Survey and Geospatial Data for Geographical Targeting of the Poor and Vulnerable: Evidence from Malawi

M. Gualavisi, David Newhouse
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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.
整合调查和地理空间数据,对贫困和弱势群体进行地理定位:马拉维的证据
为解决发展中国家最贫困村庄的识别难题,本研究引入了一种具有成本效益的策略,将家庭消费调查、地理空间数据和部分登记册结合起来加以利用。本研究模拟了部分登记册,其中包含马拉维 10 个贫困地区 450 个村庄的数据,并包含替代贫困指标。这些指标被用来估算家庭人均消费,而家庭人均消费又被用来利用公开的地理空间数据训练预测模型。根据 2016 年住户调查得出的村庄福利估算参考值对该方法进行了评估。部分登记方法与三种替代方法进行了基准比较:替代均值测试得分、元相对财富指数以及来自带有地理空间指标的住户调查的预测。结果显示,部分登记模型与实际福利措施的等级相关性为 0.75,明显优于其他方法,其他方法的相关性在-0.02 到 0.2 之间。这些发现在各种稳健性检验(包括添加高斯噪声)中都是成立的,表明在低收入地区收集家庭层面的代理贫困数据可以显著提高整合调查和卫星图像数据的机器学习模型的性能,从而实现村级地理定位。
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