Togo Made a Digital Government Stimulus System In Two Weeks

An anonymous reader shares an excerpt from a Bloomberg report: In Togo, a nation of about 8 million people where the average income is below $2 a day, it took the government less than two weeks to design and launch an all-digital system for delivering monthly payments to about a quarter of the adult population. People [...] with no tax or payroll records, were identified as in need, enrolled in the program, and paid without any in-person contact. According to Anit Mukherjee, a policy fellow at the Center for Global Development, "the U.S. program looks like a dinosaur" in comparison. [The program called Novissi], which means "solidarity" in the local Ewe language, is the brainchild of Cina Lawson, who heads the Ministry of Digital Economy and Digital Transformation. [...] Togo had run some cash transfer programs in the past, but they were small-scale and typically involved registering households one at a time and distributing physical money by hand. According to [Shegun Bakari, a close adviser to the president], other cabinet members objected to the idea of using mobile technology, arguing that many in rural areas didn't have access to phones or identification, and even those who did might lack the wherewithal to navigate a digital system. Yet in fact, Togolese -- like people across Africa -- had for years been using "mobile money," stored on and transferred from their mobile phones. The president quickly embraced the proposal. [....] Covid pushed countries to move quickly beyond age-old debates over who is deserving of government aid and whether transfers should be unconditional. The sheer breadth of suffering undercut the paternalistic attitude that the poor brought their suffering upon themselves. Even with the president's support, Lawson's team faced big challenges. For starters they didn't know which Togolese were most in need: Tax rolls were no help in a country where four out of five working-age people toil in the informal economy. The last national census, conducted almost a decade earlier, hadn't gathered information about households' wealth or income. To ensure payments were made only to verified individuals, the team sought to build the platform off an existing database. Few Togolese possessed a driver's license or national ID card, but 3.6 million adults are registered to vote, according to the country's electoral commission, which requires potential voters to indicate their occupation and address. This electoral database was thought to represent somewhere between 83% and 98% of the adult population. Lawson and other members of the cabinet decided to focus the first round of support on anyone with an address in greater Lome who had listed an informal occupation, including shopkeepers, seamstresses, maids, hairdressers, and drivers. With the funding allocated by the government, they could provide each beneficiary one-third of the minimum wage, about $20 per month. Lawson insisted that the platform be able to offer an instantaneous payoff; otherwise, she warned, Togolese would doubt the promise of "free money" and fail to enroll. "You register, the platform determines you're eligible -- because once you enter your voter ID, the platform knows your profession and your geographic position -- and bam! You receive an SMS with the money," she says. The program wasn't without hiccups, however. When Novissi first began on April 8th, there were millions of registration attempts and tens of thousands of people calling for troubleshooting help, causing the platform to briefly buckle. But, as the report notes, it "largely worked," with more than 567,000 people receiving payments in the first round of disbursements. "In part because Novissi proved so successful, the ministry teamed up with GiveDirectly and researchers at the University of California at Berkeley to fund a round of payments for the 200 poorest cantons," adds Bloomberg. "To find them, the researchers trained an algorithm to identify impoverished communities based on their urban layout and housing materials, using satellite images. The researchers couldn't pick individual beneficiaries by occupation because many rural residents didn't have differentiated professions; instead, they created a second algorithm that used data from mobile phones -- including the frequency and timing of calls, texts, and data use -- to identify the poorest users. Over the next few months, this round pushed funds out to 138,000 more beneficiaries."

Read more of this story at Slashdot.