Medium-term changes in the patterns of internal population movements in Latin American countries: effects of the COVID-19 pandemic

The report analyzes the medium-term changes in internal population movements in Latin America due to the COVID-19 pandemic, focusing on Argentina, Chile, and Mexico from March 2020 to May 2022.

(Generated with the help of GPT-4)

Quick Facts
Report location: source source 2
Language: English
Publisher: Economic Commission For Latin America And The Caribbean
Authors: Andrea Nasuto, Carmen Cabrera-arnau, Miguel González-leonardo, Ruth Neville, Francisco Rowe
Time horizon: 2020
Geographic focus: Argentina, Chile, Mexico

Methods

The research employed a data-driven approach, utilizing anonymized mobile phone location data from Meta-Facebook users to analyze internal population movements across Argentina, Chile, and Mexico over a 26-month period from March 2020 to May 2022.

(Generated with the help of GPT-4)

Key Insights

The COVID-19 pandemic significantly impacted internal population movements in Latin America, particularly in Argentina, Chile, and Mexico. Utilizing anonymized mobile phone location data from Meta-Facebook users, the study examines changes in movement patterns over a 26-month period. The findings reveal a systematic decline in both short- and long-distance movements during the pandemic's early stages, with the most significant reductions occurring in densely populated areas. By March 2022, mobility levels began to recover but remained below pre-pandemic levels. The research indicates a trend of urban exodus, particularly in Argentina and Mexico, where negative net balances of short-distance movements were observed in capital cities, suggesting suburbanization. In contrast, Chile experienced limited changes in short-distance movements but recorded temporary net losses in long-distance movements. The study highlights the persistence of these patterns and the potential long-term effects of the pandemic on population mobility in the region, emphasizing the need for further research to understand the underlying causes and implications for urban planning and policy.

(Generated with the help of GPT-4)

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Last modified: 2025/12/03 03:56 by davidpjonker