Withdrawn - 15. Introducing the Migration Archetype Model (MAM): A Data-Driven Framework for Disaster-Induced Migration Analysis in the U.S. Virgin Islands

The U.S. Virgin Islands (USVI), a U.S. territory in the Caribbean, experienced a significant 20% population decline between 2010 and 2020. Much of this shift was driven by outmigration following three major disruptive events: the 2012 closure of the HOVENSA oil refinery (economic disaster), the 2017 Category 5 Hurricanes Irma and Maria (natural disaster), and the COVID-19 pandemic beginning in 2020 (biological disaster). In response to these events and their layered impact on labor, family, and cultural structures, we developed the Migration Archetype Model (MAM)-a multidisciplinary framework for analyzing disaster-induced migration in isolated, non-American Community Survey jurisdictions like the USVI.
MAM draws on decennial census data, labor studies, and microdata from the American Community Survey (ACS) to characterize migration flows by demographic traits, household composition, educational attainment, and employment sector. The model uses a symbolic structure grounded in human anatomy-head, torso, seat, and feet-to frame the impacts of migration across key dimensions such as brain drain, labor force disruption, family fragmentation, and sustainability.
This poster introduces MAM to a national research audience and presents its application across three distinct periods of migration. We show how ACS microdata, particularly Place of Birth and Year of Entry, can be effectively leveraged to analyze intra-national migration in territories excluded from the ACS sampling frame. Results demonstrate distinct migration archetypes by gender, age, race, and occupational background, with varied implications across disaster types. Finally, we discuss how MAM can inform public policy, economic recovery planning, and statistical modeling of migration trends in disaster-prone or geographically isolated regions.

migration modeling

disaster resilience

U.S. territories

population statistics

ACS microdata

gender and migration 

Mid-Level

Knowledge

Women in Statistics and Data Science 2025