Predicting U.S. migration flows with online search data

Bibliographic Data

ID24225938
AuthorsJaeyong Yoo (0000-0002-1212-5121, Virginia Tech, Blacksburg, VA, USA), Patryk Babiarz (0000-0003-2506-381X, University of Georgia, Athens, GA, USA)
Year2026
Publication date2026-10-16
Open AccessNo
TypeARTICLE
VenueUrban Studies (JOURNAL)
Journal identifiersISSN: 0042-0980 • E-ISSN: 1360-063X
PublisherSAGE Publications (PUBLISHER • US)
DOI10.1177/00420980261493801
LanguageEN
References cited69

Residential mobility shapes labor markets, housing demand, infrastructure needs, and regional development in the United States. Policymakers rely on official migration data to monitor population movements, but these data are often released after the underlying moves have occurred. Because many households search online for jobs, housing, cost of living, schools, climate, and other destination-specific information before moving, online search activity may provide early signals of migration interest. This study examines whether Google Trends data improve the prediction of U.S. interstate migration flows. Using Internal Revenue Service state-to-state migration records and Google Trends data for all 50 states and the District of Columbia from 2004 to 2018, the study estimates gravity-based panel models of unilateral out-migration and bilateral origin-destination flows. The results show that migration-related search intensity is associated with subsequent migration flows and improves predictive performance beyond conventional gravity-model covariates and fixed effects. These findings suggest that Google Trends data can serve as a timely complement to administrative migration records. Search data do not replace official statistics, but they can help identify emerging migration patterns before administrative data become available.

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