Year: 2023
Type: article
Abstract: Motivated by debates about California’s net migration loss, we employ valued exponential-family random graph models to analyze the inter-county migration flow network 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. This paper demonstrates how generative statistical models can provide mechanistic insights beyond simple hypothesis-testing.
Authors: Peng Huang, Carter T. Butts
Institution: University of California, Irvine
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
SDG: Reduced inequalities
Open Access status: hybrid
OpenAlex ID: W4389021494