Fast Company highlights Togo’s nation-wide social protection program (Novissi), which was designed in collaboration with CEGA Faculty Co-Director Josh Blumenstock to target Togo’s poorest citizens with cash transfers for COVID-19 relief:
“GiveDirectly, a charity that has focused for just under a decade on direct cash transfers to people in poverty around the world, particularly in Africa, has been escalating its pandemic relief efforts—and continually innovating with partners to find groundbreaking ways to target the most in need of money. The charity’s latest innovation is harnessing an algorithm, designed by UC Berkeley, that uses artificial intelligence to identify the poorest individuals in the poorest areas, and transfer cash relief directly to them…
…The project launched in November in Togo, one of the poorest countries in the world, where it’s estimated that 55% live below poverty line. That number is closer to 81% among the rural population, who are the focus of this pilot. In April, in response to COVID-19, Togo’s government established an innovative cash transfer system called Novissi, spearheaded by Cina Lawson, Togo’s Minister of Postal Affairs and Digital Economy. This allowed the government to send cash relief via mobile to approximately 12% of the population. So, an infrastructure already exists, but they wanted to expand it to more rural areas, where it’s harder to pick out the most in need without the right technology.
Josh Blumenstock, associate professor at the UC Berkeley School of Information, first wrote the paper on mapping poverty using satellite and mobile data in 2017, before meeting Chia to discuss application. Blumenstock stresses that they are not telling the tool what to look for; rather, machine learning enables it to recognize patterns itself. In order to train it on what to look for, they surveyed a large sample of 15,000 citizens across the poorest 100 of Togo’s cantons, on which the government wanted to focus, asking them “a rich set of questions about their socioeconomic status,” including on their income and spending, and whether they’d missed meals in the last week. They paired this with their mobile phone data to help the algorithm find patterns, which it could then scale up to the entire populations of those cantons.”
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