Inferring Fine-grained Migration Patterns across the United States

Fine-grained migration data illuminate important demographic, environmental, and health phenomena. However, migration datasets within the United States remain lacking: publicly available Census data are neither spatially nor temporally granular, and proprietary data have higher resolution but demographic and other biases. To address these limitations, we develop a scalable iterative-proportional-fitting based method that reconciles high-resolution but biased proprietary data with low-resolution but more reliable Census data. We apply this method to produce MIGRATE, a dataset of annual migration matrices from 2010-2019 that captures flows between 47.4 billion pairs of Census Block Groups — about four thousand times more granular than publicly available data. These estimates are highly correlated with external ground-truth datasets, and improve accuracy and reduce bias relative to raw proprietary data. We use MIGRATE to analyze both national and local migration patterns. Nationally, we document temporal and demographic variation in homophily, upward mobility, and moving distance: for example, we find that people are increasingly likely to move to top-income-quartile CBGs and identify racial disparities in upward mobility. We also show that MIGRATE can illuminate important local migration patterns, including out-migration in response to California wildfires, that are invisible in coarser previous datasets. We publicly release MIGRATE to provide a resource for migration research in the social, environmental, and health sciences.