Measuring Global Migration Flows using Online Data

• URL: http://arxiv.org/abs/2504.11691v1
• Date: Wed, 16 Apr 2025 01:19:26 GMT
• Title: Measuring Global Migration Flows using Online Data
• Authors: Guanghua Chi, Guy J. Abel, Drew Johnston, Eugenia Giraudy, Mike Bailey,
• Abstract summary: Using privacy protected records from three billion Facebook users, we estimate country-to-country migration flows at monthly granularity for 181 countries.<n>We estimate that 39.1 million people migrated internationally in 2022 (0.63% of the population of the countries in our sample)<n>To support research and policy interventions, we will release these estimates publicly through the Humanitarian Data Exchange.
• Score: 0.38836072943850625
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 pre-crisis rate. We also find that migration from Ukraine increased tenfold in the wake of the Russian invasion. To support research and policy interventions, we will release these estimates publicly through the Humanitarian Data Exchange.

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