When the novel coronavirus Arriving in Togo in March, its leaders, like those of many countries, responded with stay-at-home orders to suppress contagion and an economic assistance program to replace lost income. But the way Togo targeted and delivered this aid was in some ways more tech-centric than many larger, richer countries. No one received a paper check in the mail.
Instead, the Togolese government quickly put in place a system to support its poorest populations with mobile cash payments – a technology more established in Africa than in rich countries said to be at the forefront of mobile technology. The most recent payments, funded by the nonprofit GiveDirectly, were targeted with help from machine learning algorithms, which look for signs of poverty in satellite photos and cell phone data.
The Togo project is an example of the pandemic forcing urgent experimentation that can lead to lasting change. The shift to satellite and mobile data is partly due to a dearth of reliable data on citizens and their needs. Shegun Bakari, an adviser to the Togolese president, says it has worked so well that the data-centric approach will likely be used more widely. “This project is fundamental for us on how we can set up our social protection system in Togo in the future,” he says.
The new help system is called EXTREME, meaning “solidarity” in the local Ewe language, and materialized during 10 days of intense work which began at the end of March. Cina Lawson, Togolese Minister of Digital Economy, was motivated by fear of the side effects of pandemic shutdowns. Half of Togo’s 8 million inhabitants live on less than $ 1.90 a day. Most Togolese work in the so-called informal sector, for example as workers or as seamstresses, and Covid-19 restrictions sharply cut their income. “We thought we had to support these people because if they don’t die from Covid, they will starve,” Lawson says.
Novissi launched on April 8 and sent aid the same day to informal workers in and around Togo’s capital, Lomé. The radio ads asked people to text a special number that gave them a short quiz via SMS. Payments were sent more or less instantly, if a check of Togo’s voter identification database, which covers 93% of the population, confirmed that a person had previously declared informal occupation and lived in an eligible area. . The program was quickly extended to the area around Togo’s second largest city, Sokodé.
Men received 10,500 CFA francs each month, or about $ 20, in bimonthly installments, and women 12,250 CFA francs, about $ 23; the difference was intentional to better support families. The amounts were intended to replace about a third of the minimum wage in Togo. So far, the government has sent around $ 22 million via Novissi to nearly 600,000 people.
Lawson was proud to see government aid sent so quickly, but as Covid-19 spread, she was also concerned that her program could target those who needed it most, in part because she didn’t know where find them. Government officials reached out to Joshua Blumenstock, co-director of the Center for Effective Global Action at UC Berkeley University, who had studied how big data can fill the information gaps faced by countries like Togo. His lab had shown that phone records could predict individual wealth in Rwanda on in-person and in-person investigations, and that satellite images could follow poverty zones in sub-Saharan Africa.
Blumenstock offered to adapt its technology to help it and enlisted a team made up of graduate students from Berkeley, two faculty members from Northwestern and the nonprofit Innovations for Poverty Action. He also connected Lawson with GiveDirectly, which distributes cash payments in poor countries. GiveDirectly had previously spoken with Blumenstock about using their work to prioritize help and now saw a chance to put the idea into action.
GiveDirectly payments typically reflect information collected by staff members who visit poor communities and conduct household surveys. But it posed risks during a pandemic. Han Sheng Chia, the organization’s director of special projects, was curious whether satellite data and the like could help the group distribute aid faster and more widely. “The scale of the needs we are facing this year is so huge,” he says. The World Bank estimated in October that the number of people living in extreme poverty will increase by about 100 million this year, the first global increase in 20 years.