Poverty traps in Africa
About 900 million people—one-third in Africa—live in extreme poverty. Operating on the assumption that life in impoverished communities is fundamentally so different that it can trap people in cycles of deprivation (‘poverty traps’), major development agencies such as the World Bank have deployed a stream of development projects to break these cycles (‘poverty targeting’). However, scholars are currently unable to answer questions such as in what capacity do poverty traps exist; to what extent do these interventions release communities from such traps—as they are held back by methodological challenges.My aim in this project is to identify to what extent African communities are trapped in poverty and explain how competing development interventions alter these communities’ prospects to free themselves from deprivation.To achieve this aim, I will (i) train machine learning algorithms to identify poverty traps from satellite images between 1990s to 2020; (ii) use these remote sensing derived poverty data to examine how World Bank versus Chinese development programs target and affect communities; (iii) using this foundational work, scale up the results from (i) and (ii) to validate them and develop a theory of the varieties of poverty traps and targeting. In a final step (iv), we will develop an R package, PovertyMachine, that will be able to produce estimates of poverty traps and conduct program evaluations, thus ensuring open-access for researchers to our innovative methods.
Fredrik Johansson (contact)
Chalmers, Computer Science and Engineering (Chalmers), Data Science
University of Gothenburg
Project ID: 2019-01120
Funding Chalmers participation during 2020–2022
Related Areas of Advance and Infrastructure