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

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. A machine 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.

Beyond the Walls: Patterns of Child Labour, Forced Labour, and Exploitation in a New Domestic Workers Dataset

“…Understanding the true nature and extent of exploitation in any given local or national context is fraught with methodological difficulties, stemming from the fundamental problem that victims of human trafficking, forced labour, and modern slavery are a hardto-find population. But recent innovations have delivered the most reliable estimates of modern slavery's scale to date: Multiple Systems Estimation (MSE) [34,35], multi-level modelling based on random sample survey instruments [4], the network scale-up method (NSUM) [36], and hyperlocal street surveys combined with non-standard data into insight tiles [37].…”

“…To achieve fine-grained results at the hyperlocal level, another research team introduced the innovation of combining street-level data with non-standard data streams to generate a forced labour heat map for the city of Dar es Salaam, Tanzania [37]. Dar es Salaam has a population of over 6 million across 90 administrative wards.…”

“…Sometimes termed organic data [44], naturally occurring data [45], or data in the wild [46], these are data not intended to support prevalence estimates, assessments of vulnerability/risk factors, or other statistical analysis but which nonetheless can be analysed for key research findings. The study in Tanzania [37] contained one example of "found" data, the layers of telecommunications and transport data used as proxies for vulnerability, and the New Orleans study contains another-victim lists held by service providers [35].…”

Impacto socioeconómico de la migración en Cuba, 2022

“…El efecto de contagio del miedo a la migración entre países, contribuye a la literatura económica proporcionando un nuevo estudio basado en migración, índices de miedo y datos sociales (Guenichi et al 2022). Comprender qué factores predicen si un migrante terminará o no en un barrio desfavorecido podría ayudar a prevenir la explotación de personas vulnerables (Lavelle-Hill et al 2022).…”