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

• DOI: 10.48550/arxiv.2305.06888
• Publication Date: 2023-01-01
• Authors: Lorenzo Lucchini; Ollin D. Langle-C...; Lorenzo Candeago; Lucio Melito; Alex Chunet; Aleister Montfort; Bruno Lepri; Nancy Lozano-Gracia; Samuel P. Fraiberger
• Keywords: FOS: Computer and information sciences; Physics - Physics and Society; Human mobility; GPS data; General Economics (econ.GN); Computer applications to medicine. Medical informatics; 1. No poverty; R858-859.7; COVID-19; FOS: Physical sciences; Physics and Society (physics.soc-ph); 3. Good health; Developing countries; FOS: Economics and business; Computer Science - Computers and Society; 11. Sustainability; Computers and Society (cs.CY); Economics - General Economics

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.<br/>Supplementary Information in support to the main manuscript can be found at https://doi.org/10.6084/m9.figshare.23639079.v1<br/>