California Exodus? A Network Model of Population Redistribution in the United States

TL;DR

This paper uses advanced network models to analyze migration patterns in the US, revealing that racial dynamics and urbanization influence California's population loss, while political climate and housing costs are less impactful.

Contribution

It introduces a novel protocol for visualizing migration mechanisms and applies in silico experiments to quantify their effects on California's exodus.

Findings

Racial dynamics contribute to California's migration loss.

Urbanization reduces the exodus.

Political climate and housing costs have minimal impact.

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 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.