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.

Partner organizations

  • University of Gothenburg (Academic, Sweden)
  • University of Gothenburg (Publisher, Sweden)
Start date 01/01/2020
End date 31/12/2022

Page manager Published: Fri 07 Aug 2020.