Huang Peng, Butts Carter T
University of California, Irvine.
J Math Sociol. 2024;48(3):311-339. doi: 10.1080/0022250X.2023.2284431. Epub 2023 Nov 26.
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 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.
受关于加利福尼亚州净移民损失的辩论启发,我们采用有价值的指数族随机图模型来分析美国各县之间的移民流动网络。我们引入了一种协议,以可视化潜在潜在机制的复杂影响,并进行剔除实验以量化它们对加利福尼亚州人口外流的贡献。我们发现种族动态导致了加利福尼亚州的人口外流,城市化缓解了这一现象,而政治气候和住房成本影响不大。此外,加利福尼亚州人口外流的严重程度取决于衡量方式,并且加利福尼亚州并非人口损失最严重的州。本文展示了生成性统计模型如何能够提供超越简单假设检验的机制性见解。