By Joshua Blumenstock
Last week, I had an unexpected encounter on Zoom. The callers were senior government officials from the Togolese Republic, in West Africa.
Why? The president of Togo wants to send cash payments (equivalent to around US$20 per person) to around 525,000 vulnerable Togolese households this month. But, like most developing countries, Togo lacks good data on the economic situation of specific households, and certainly has no way of collecting this information in the middle of a pandemic. Cina Lawson, a cabinet minister in Togo, and Shegun Adjadi Bakari, one of the president’s senior advisers, called to find out how big data and machine learning might help them to find the people who need the payments most.
COVID-19’s spread and lockdowns in low-income countries are leaving hundreds of millions of poor and vulnerable people without work or income. The United Nations World Food Programme has warned of devastating famines — 265 million people in low- and middle-income countries are projected to suffer from acute hunger by the end of the year...
Read the full article, originally published as “Machine learning can help get COVID-19 aid to those who need it most” by Nature Research on May 14, 2020.
Joshua Blumenstock is an Assistant Professor at the UC Berkeley School of Information. His research lies at the intersection of machine learning and empirical economics.
