Using Data Science to Target Cash Transfers for COVID-19 Relief

7. Give Directly

SUBMITTED BY
GiveDirectly and Center for Effective Global Action

PARTNERS
Data-Intensive Development Lab (DIDL) at UC Berkeley

LOCATION
Togo


On the road to recovery from COVID-19, many countries lack reliable and up-to-date information about economic conditions on the ground and have no way of collecting it during a pandemic. Traditional aid modalities relying on in-person enrollment and delivery are no longer safe or scalable, with governments and NGOs lacking personnel and relief taking weeks to arrive. 

GiveDirectly (GD) and the Center for Effective Global Action at UC Berkeley (CEGA) propose addressing this challenge by developing and testing a new model for humanitarian support that enables cash transfers to be deployed effectively, accurately, and at scale to those who need them most. This project incorporates new data and computational technologies to identify people and places in economic distress and integrating data from mobile phones, satellite imagery, and traditional surveys. The plan will pilot in one or more low and middle-income countries and develop a transparent framework to scale globally.