  Motivated by debates about California's net migration loss, we employ valued
exponential-family random graph models to analyze the inter-county migration
flow networks in the United States. We introduce a protocol that visualizes the
complex effects of potential underlying mechanisms, and perform in silico
knockout experiments to quantify their contribution to the California Exodus.
We find that racial dynamics contribute to the California Exodus, urbanization
ameliorates it, and political climate and housing costs have little impact.
Moreover, the severity of the California Exodus depends on how one measures it,
and California is not the state with the most substantial population loss. The
paper demonstrates how generative statistical models can provide mechanistic
insights beyond simple hypothesis-testing.
