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Helping Design Togo’s Flagship Cash Transfer Program

Data Science for Development
cell phone mobile money Uganda
Mobile Money Street Shot, Uganda. Photo Credit: Gunnar Salvarsson

Machine learning, combined with digital data, can help identify poor for cash transfer delivery

In response to the COVID-19 pandemic, CEGA co-Director Joshua Blumenstock has been helping design Togo’s flagship social protection program, Novissi, using insights from machine learning.

Blumenstock’s team at the Data-Intensive Development Lab, in collaboration with Emily Aiken (UC Berkeley), Suzanne Bellue (University of Mannheim), and Dean Karlan and Chris Udry (Northwestern), are using mobile phone and satellite data, combined with machine learning, to help identify those Togolese citizens with the greatest need for humanitarian support. The government and the NGO GiveDirectly is then transfering cash aid, delivered via mobile money, to the individual’s identified by Blumenstock’s team.

To date, over 550,000 Togolese individuals have received direct and unconditional cash transfers of roughly $20/month (around a third of the income of a Togolese earning minimum wage), delivered via mobile money to their mobile phones. Blumenstock’s team is working with the Government of Togo and GiveDirectly to expand this program in rural areas, and have already targeted over $10 million in cash transfers to over 140,000 Togolese in danger of food insecurity.

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