Targeting is a central challenge in the design of humanitarian programs: given available data, how does one identify the individuals and households with the greatest need for assistance? Here we show that machine learning, applied to non-traditional data from satellites and mobile phones, can improve the targeting of anti-poverty programs. Our analysis is based on data from three field-based projects—in Togo, Afghanistan, and Kenya—that illustrate the promise, as well as some of the potential challenges, of this new approach to targeting. Collectively, the results highlight the potential for new data sources to improve humanitarian response efforts, particularly in crisis settings when traditional data are missing or out of date.

Cash transfer programs are the prime example of a social innovation that has scaled rapidly across the globe. Such programs often have multiple objectives, from providing short-term safety nets to the long-term ambition of breaking the intergenerational transmission of poverty. A lot of empirical evidence has been accumulated on the short term goals. 25 years after the start of the first national programs in Mexico and Brazil, we can also start taking stock of the long-term objectives. This presentation will first discuss the methodological challenges for studying long-term returns, focusing on common pitfalls and potential solutions. It will then lay out a framework to consider the different pathways and mechanisms towards long-term returns to clarify the type of outcomes and contextual variables to measure both on the intermediate and long run. Given the complexity and multi-dimensionality of the theory-of-change for long-term impacts, we then draw lessons from different studies, following the framework. We first highlight the evidence on long-term returns to conditional cash transfers (CCTs) that operate through returns on investments in education and skills. A lot of this evidence draws on the first generation of CCTs in Latin America, but we also link to the emerging evidence from other regions. We then turn to evidence on unconditional cash transfers (UCTs), which is particularly prominent for Sub-Saharan Africa, focusing on a wider set of investments and behavioral changes with evidence on impacts years after the end of the transfers.