Machine Learning and Mobile Phone Data Can Improve the Targeting of Humanitarian Assistance

The COVID-19 pandemic has devastated many low- and middle-income countries (LMICs), causing widespread food insecurity and a sharp decline in living standards. In response to this crisis, governments and humanitarian organizations worldwide have mobilized targeted social assistance programs. Targeting is a central challenge in the administration of these programs: given available data, how does one rapidly identify the individuals and families with the greatest need? This challenge is particularly acute in the large number of LMICs that lack recent and comprehensive data on household income and wealth. Here we show that non-traditional “big” data from satellites and mobile phone networks can improve the targeting of anti-poverty programs.