Most stay close, some go far: understanding migration distance in West Africa

Migration distances vary widely across individuals and contexts, yet empirical evidence on the drivers of migration distance within Africa remains limited. While much of the migration literature focuses on Western destinations, less is known about how far migrants travel within the African continent and what factors are associated with these trajectories. This study addresses this gap using a large micro-level dataset collected by the International Organization for Migration between 2021 and 2023, comprising over 60,000 land-based migration observations from eight West African countries. We pursue two objectives. First, we assess the extent to which intended migration distance can be predicted using information available at transit points. Second, we identify and rank the key individual and contextual predictors associated with intended migration distance using interpretable machine learning and statistical models. The analysis reveals a bimodal distribution of migration distances: most individuals move locally, typically within 100 km, while a smaller yet significant share undertake long-distance journeys exceeding 1500 km. Employment status, GDP (at the residence locality) and reasons for travel are among the most influential predictors of intended migration distance. Unemployed migrants tend to travel significantly farther than employed individuals, suggesting persistent economic constraints and opportunity-seeking behavior. In contrast, conflict-related mobility shows marked temporal variability with a sharp increase in long-distance migration in 2023, coinciding with intensified violence in key origin countries. These patterns suggest an asymmetry between economic and conflict-driven mobility: economic pressures are associated with relatively stable distances traveled, whereas conflict introduces greater volatility and route reconfiguration. By combining rare field data with interpretable machine learning, this study advances quantitative understanding of mobility dynamics in West Africa and provides evidence relevant to migration forecasting and humanitarian policy in underrepresented regions.

Authors

Institutions

Publication Details

Journal
EPJ Data Science
Published
2026-03-13
DOI
https://doi.org/10.1140/epjds/s13688-026-00633-6
Primary Topic
Global Maritime and Colonial Histories
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Most stay close, some go far: understanding migration distance in West Africa

Massimo Riccaboni, Irene Tafani, Ola Ali, Rafael Prieto-Curiel
EPJ Data Science
Global Maritime and Colonial Histories
article

Most stay close, some go far: understanding migration distance in West Africa

Massimo Riccaboni, Irene Tafani, Ola Ali, Rafael Prieto-Curiel
article en

Abstract

Migration distances vary widely across individuals and contexts, yet empirical evidence on the drivers of migration distance within Africa remains limited. While much of the migration literature focuses on Western destinations, less is known about how far migrants travel within the African continent and what factors are associated with these trajectories. This study addresses this gap using a large micro-level dataset collected by the International Organization for Migration between 2021 and 2023, comprising over 60,000 land-based migration observations from eight West African countries. We pursue two objectives. First, we assess the extent to which intended migration distance can be predicted using information available at transit points. Second, we identify and rank the key individual and contextual predictors associated with intended migration distance using interpretable machine learning and statistical models. The analysis reveals a bimodal distribution of migration distances: most individuals move locally, typically within 100 km, while a smaller yet significant share undertake long-distance journeys exceeding 1500 km. Employment status, GDP (at the residence locality) and reasons for travel are among the most influential predictors of intended migration distance. Unemployed migrants tend to travel significantly farther than employed individuals, suggesting persistent economic constraints and opportunity-seeking behavior. In contrast, conflict-related mobility shows marked temporal variability with a sharp increase in long-distance migration in 2023, coinciding with intensified violence in key origin countries. These patterns suggest an asymmetry between economic and conflict-driven mobility: economic pressures are associated with relatively stable distances traveled, whereas conflict introduces greater volatility and route reconfiguration. By combining rare field data with interpretable machine learning, this study advances quantitative understanding of mobility dynamics in West Africa and provides evidence relevant to migration forecasting and humanitarian policy in underrepresented regions.

EPJ Data Science
IMT School for Advanced Studies Lucca (IT), Istituto Universitario di Studi Superiori di Pavia (IT), Complexity Science Hub (AT)
Reduced inequalities
Openalex Percentile: Top 33%
Global Maritime and Colonial Histories
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.