AN ANALYSIS OF INTERSTATE MIGRATION PATTERNS INTO TEXAS USING IRS DATA.

Abstract

Interstate migration has become a primary driver of state-level population and economic growth in the United States, with Texas consistently ranking among the largest net recipients of domestic migrants over the past decade. This study uses Internal Revenue Service (IRS) Statistics of Income (SOI) migration data, supplemented by Census Bureau population estimates and state-level tax and cost-of-living covariates, to characterize the volume, origin, income composition, and destination geography of interstate migration into Texas between filing years 2013–2014 and 2022–2023. A panel of 510 origin-state-year observations was constructed and used to estimate a gravity-style fixed-effects regression model and a random forest regression model predicting (i) the net migration rate per 100,000 origin-state population and (ii) the mean adjusted gross income (AGI) per in-migrating household. The gravity model achieved R² = 0.78 for migration rate and R² = 0.82 for AGI per migrant, while the random forest model achieved R² = 0.89 and R² = 0.91 respectively. California, Florida, New York, Illinois, Louisiana, and Colorado together accounted for the majority of identifiable in-migration to Texas, while county-level analysis confirmed that suburban counties surrounding Dallas–Fort Worth, Houston, and Austin captured the majority of net domestic migration even as core urban counties such as Harris and Dallas recorded net domestic out-migration. State income-tax differential and origin-state population size emerged as the dominant predictors of migration intensity, while cost-of-living differential was the strongest predictor of the income composition of in-migrants. These findings provide a quantitative basis for state and local economic-development planning, infrastructure forecasting, and tax-policy evaluation.