https://doi.org/10.1140/epjds/s13688-025-00532-2
Research
Socioeconomic disparities in mobility behavior during the COVID-19 pandemic in developing countries
1
Centre for Social Dynamics and Public Policy, Bocconi University, Milan, Italy
2
Institute for Data Science and Analytics, Bocconi University, Milan, Italy
3
World Bank Group, Washington, DC, USA
4
Fondazione Bruno Kessler, Trento, Italy
5
University of California at Berkeley, Berkeley, CA, USA
6
University of Vermont, Burlington, VT, USA
7
Massachusetts Institute of Technology, Cambridge, MA, USA
8
New York University, New York City, NY, USA
a
lorenzo.lucchini@unibocconi.it
b
sfraiberger@worldbank.org
Received:
23
August
2024
Accepted:
12
February
2025
Published online:
24
March
2025
Mobile phone data have played a key role in quantifying human mobility during the COVID-19 pandemic. Existing studies on mobility patterns have primarily focused on regional aggregates in high-income countries, obfuscating the accentuated impact of the pandemic on the most vulnerable populations. Leveraging geolocation data from mobile-phone users and population census for 6 middle-income countries across 3 continents between March and December 2020, we uncovered common disparities in the behavioral response to the pandemic across socioeconomic groups. Users living in low-wealth neighborhoods were less likely to respond by self-isolating, relocating to rural areas, or refraining from commuting to work. The gap in the behavioral responses between socioeconomic groups persisted during the entire observation period. Among users living in low-wealth neighborhoods, those who commute to work in high-wealth neighborhoods pre-pandemic were particularly at risk of experiencing economic stress, facing both the reduction in economic activity in the high-wealth neighborhood and being more likely to be affected by public transport closures due to their longer commute distances. While confinement policies were predominantly country-wide, these results suggest that, when data to identify vulnerable individuals are not readily available, GPS-based analytics could help design targeted place-based policies to aid the most vulnerable.
Key words: Human mobility / GPS data / COVID-19 / Developing countries
Supplementary Information The online version contains supplementary material available at https://doi.org/10.1140/epjds/s13688-025-00532-2.
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