In addition to its devastating public health impacts, the COVID-19 pandemic has exacerbated inequality around the world. Poor populations have disproportionately experienced COVID-19 infection while enduring greater financial hardship as a result of lockdown orders and business closures. And despite increasing vaccination rates in many countries, the poor are also often the hardest to reach and the last to receive COVID-19 vaccinations. At the same time, the ability to effectively assist poor populations in many countries remains hindered by the lack of a solid answer to a simple question—where these populations reside.

In many parts of the world, poverty datasets are out of date or exist only at high levels. These data often come from census datasets or other household surveys that may be 10 or more years old. However, there is a strong body of literature that suggests that nontraditional data sources can help fill information gaps on poverty. These nontraditional sources can serve as proxies for where poor people live, whether it be because areas with high smartphone penetration tend to be wealthier, or that areas further away from roads and other infrastructure tend to be poorer.

Over the past four years, Facebook’s Data for Good team and the University of California, Berkeley have been working to develop micro-estimates of wealth and poverty for low- and middle-income countries using nontraditional data. The estimates are built by applying machine-learning algorithms to a range of nontraditional data including satellite imagery, topographic maps, and de-identified connectivity data from Facebook.

Read the full article about AI poverty maps by  Laura McGorman, Guanghua Chi, and Han Fang at Brookings.