Using Mobile Phone and Satellite Data to Target Emergency Cash Transfers

February 16, 2021

Since March 2020, our research team has been helping the Government of Togo get cash to the poorest people in the country, using a mix of machine learning, satellite imagery, and mobile phone data. This article provides a non-technical summary of those efforts.

Background: Togo and the COVID-19 crisis 

In Togo, a small country in West Africa, over 50% of the population lives in poverty. The COVID-19 pandemic threatens to reverse years of poverty reductions, as lockdown measures designed to stop the spread of the virus limit economic activity and threaten food security. More broadly, the World Bank estimates that the pandemic will force between 119 and 124 million people in low and middle-income countries into extreme poverty in 2020. 

In response to this crisis, the Government of Togo launched “Novissi,” an innovative social assistance program designed to provide emergency cash assistance to Togo’s neediest families. Over the course of just a few weeks, the government built and deployed a completely contactless, digital system that provided over half a million individuals with cash. Beneficiaries registered using their mobile phones; after entering basic information into a USSD menu, they were then immediately sent mobile money transfers of approximately $20/month, lasting for three months. This program was the first of its kind, described as an “exemplary case of social protection in response to the coronavirus pandemic in Africa.”

The targeting challenge 

A key question at the heart of Novissi — and at the heart of the hundreds of other targeted social protection programs that have been launched in response to COVID-19 — is how to prioritize those people with the greatest need? The difficulty was that the Togolese Government did not have a comprehensive social registry that would allow them to directly identify and prioritize its poorest people. The last census was conducted in 2011, and even that database did not have any information that could allow the government to determine who should be prioritized for assistance. And in the middle of a pandemic, it was impractical to collect the data required to create a new registry.

When Novissi first launched in April 2020, the government chose to prioritize informal workers living in the regions most impacted by the lockdown restrictions. To determine eligibility, they relied on a voter database that had been recently updated, and in which people had self-declared their home location and their occupation. 

Our work: Guiding the expansion of Novissi 

The Government of Togo, in partnership with GiveDirectly, is currently expanding Novissi to rural areas of the country, where extreme poverty is most severe. A top priority in this expansion is ensuring that benefits are targeted toward the poorest citizens. Our team of researchers — including Emily Aiken (UC Berkeley), Suzanne Bellue (U. Mannheim), Joshua Blumenstock (UC Berkeley / CEGA / IPA), Dean Karlan (Northwestern / IPA), and Chris Udry (Northwestern / IPA) — are supporting this effort. The novel approach we have developed has two main stages.

Continued...

Read full article: 
Using Mobile Phone and Satellite Data to Target Emergency Cash Transfers 
January 11, 2021  |  Josh Blumenstock  |  Medium

Read more:

The Pandemic Pushed This Farmer Into Deep Poverty. Then Something Amazing Happened 
February 15, 2021  |  Malaka Gharib |  NPR.org

A Clever Strategy to Distribute Covid Aid—With Satellite Data:  The small nation of Togo used image analysis algorithms to target economic support for its most vulnerable residents 
December 17, 2020  |  Tom SImonite  |  Wired

How GiveDirectly is finding the poorest people in the world—and sending them cash 
December 11, 2020  |  Talib Visram  |  Fast Company