Socioeconomic disparities in mobility behavior during the COVID-19 pandemic in developing countries

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. Le...

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Bibliographic Details
Published in:arXiv.org 2024-10
Main Authors: Lucchini, Lorenzo, Langle-Chimal, Ollin, Candeago, Lorenzo, Melito, Lucio, Chunet, Alex, Montfort, Aleister, Lepri, Bruno, Lozano-Gracia, Nancy, Fraiberger, Samuel P
Format: Article
Language:English
Subjects:
Cell phones
Coronaviruses
COVID-19
Developing countries
Income
LDCs
Mobility
Pandemics
Policies
Public transportation
Rural areas
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Summary: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.
ISSN:2331-8422