Adel Daoud

About 300 million people in Africa live in extreme poverty. Given that living in impoverished communities can trap people in cycles of deprivation (‘poverty traps’), major development actors such as China and the World Bank have deployed a stream of projects to break these cycles (‘poverty targeting’). However, as scholars are held back by a data challenge, research has up until now been unable to answer fundamental questions such as whether poverty traps exist, and to evaluate what extent interventions can release communities from such traps

I am leading the AI and Global Development Lab External link, opens in new window. to identify to what extent African communities are trapped in poverty and examine how competing development programs can alter these communities’ prospects to free themselves from deprivation. Our Lab has the following objectives: (i) train image recognition algorithms—a form of AI—to identify local poverty from satellite images, 1984-2020; (ii) use these data to analyze how development actors affect African communities; (iii) use mixed methods to develop theories of the varieties of poverty traps; (iv), develop an R package, PovertyMachine, that will produce poverty estimates from new satellite images—ensuring that our innovations will benefit poverty research.