Targeting Social Protection Programs with Machine Learning and Digital Data

Social protection programs are essential to assisting the poor, but governments and humanitarian agencies are rarely resourced to provide aid to all those in need, so accurate targeting of benefits is critical. In developed economies, targeting decisions typically rely on administrative income data or broad survey-based social registries. In low-income countries, however, poverty information is rarely reliable, comprehensive, or up-to-date. Novel sources of digital data — from mobile phones and satellites, in particular — are well suited to fill this gap: they are predictive of wealth in low-income contexts and ubiquitously collected. The research studies in this dissertation design and evaluate new methods for targeting aid in low-resource contexts using machine learning, satellite imagery, and mobile phone data, and evaluate these methods in large, real-world interventions. Across social protection programs in Togo, Afghanistan, and Bangladesh, the studies in this dissertation show that targeting methods based on machine learning and digital data sources identify poor households more accurately than methods based on categorical eligibility criteria like geography or occupation, but typically less accurately than traditional survey-based poverty measurement approaches. These results highlight the potential for digital data and machine learning to improve the targeting of humanitarian aid, particularly when traditional poverty data are unavailable or out-of-date and in settings where conflict, environmental conditions, or health concerns render primary data collection infeasible. These studies also provide empirical evidence on the limitations and risks of digital and algorithmic targeting approaches, including privacy, transparency, fairness, and digital exclusion.