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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 Dean Karlan and Chris Udry from Northwestern University, 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 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 to expand this program in rural areas, with immediate plans to provide cash transfers to an additional 50,000 Togolese households in danger of food insecurity.

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