Measuring global migration flows using online data

• 成果类型: Article
• 署名作者: Chi, Guanghua; Abel, Guy J.; Johnston, Drew; Giraudy, Eugenia; Bailey, Mike
• 署名单位: University of Hong Kong; International Institute for Applied Systems Analysis (IIASA); Harvard University
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-10799
• DOI: 10.1073/pnas.2409418122
• 发表日期: 2025-05-06
• 关键词: international migration

摘要

Existing estimates of human migration are limited in their scope, reliability, and timeliness, prompting the United Nations and the Global Compact on Migration to call for improved data collection. Using privacy protected records from three billion Facebook users, we estimate country-to-country migration flows at monthly granularity for 181 countries, accounting for selection into Facebook usage. Our estimates closely match high-quality measures of migration where available but can be produced nearly worldwide and with less delay than alternative methods. We estimate that 39.1 million people migrated internationally in 2022 (0.63% of the population of the countries in our sample). Migration flows significantly changed during the COVID-19 pandemic, decreasing by 64% before rebounding in 2022 to a pace 24% above the precrisis rate. We also find that migration from Ukraine increased tenfold in the wake of the Russian invasion. To support research and policy interventions, we release these estimates publicly through the Humanitarian Data Exchange.