Using mobile money data and call detail records to explore the risks of urban migration in Tanzania

Lavelle-Hill, Rosa; Harvey, John; Smith, Gavin; Mazumder, Anjali; Ellis, Madeleine; Mwantimwa, Kelefa; Goulding, James

Abstract

Understanding what factors predict whether an urban migrant will end up in a deprived neighbourhood or not could help prevent the exploitation of vulnerable individuals. This study leveraged pseudonymized mobile money interactions combined with cell phone data to shed light on urban migration patterns and deprivation in Tanzania. Call detail records were used to identify individuals who migrated to Dar es Salaam, Tanzania’s largest city. A street survey of the city’s subwards was used to determine which individuals moved to more deprived areas. t-tests showed that people who settled in poorer neighbourhoods had less money coming into their mobile money account after they moved, but not before. Amachine learning approach was then utilized to predict which migrants will move to poorer areas of the city, making them arguably more vulnerable to poverty, unemployment and exploitation. Features indicating the strength and location of people’s social connections in Dar es Salaam before they moved (‘pull factors’) were found to be most predictive, more so than traditional ‘push factors’ such as proxies for poverty in the migrant’s source region.

Citation

Lavelle-Hill, R., Harvey, J., Smith, G., Mazumder, A., Ellis, M., Mwantimwa, K., & Goulding, J. (2022). Using mobile money data and call detail records to explore the risks of urban migration in Tanzania. EPJ Data Science, 11(8), Article 28. https://doi.org/10.1140/epjds/s13688-022-00340-y

• Journal Article Type: Article
• Acceptance Date: Apr 14, 2022
• Online Publication Date: May 8, 2022
• Publication Date: Dec 1, 2022
• Deposit Date: Jun 24, 2022
• Publicly Available Date: Jun 28, 2022
• Journal: EPJ Data Science
• Electronic ISSN: 2193-1127
• Publisher: EDP Sciences
• Peer Reviewed: Peer Reviewed
• Volume: 11
• Issue: 8
• Article Number: 28
• DOI: https://doi.org/10.1140/epjds/s13688-022-00340-y
• Keywords: Mobile Money; Machine learning; Migration; Call Detail Records; Exploitation; Tanzania; Vulnerability
• Public URL: https://nottingham-repository.worktribe.com/output/8635878
• Publisher URL: https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-022-00340-y