摘要：While migration decision-making has long been studied using mover-stayer models and standard regression models, they do not well handle small- and large-scale heterogeneities (migration propensities). The hierarchical regression model can help solve this problem, because it deals with data organized hierarchically and studies variation at different levels of the hierarchy simultaneously. Using Wisconsin’s 5% Public Use Microdata Sample (PUMS) file from Census 2000 for a two-level hierarchy – individual/household level and Public Use Microdata Area (PUMA) level, we take a fresh look at how a hierarchical logit model can improve migration studies by including demographic, socio-economic, and biogeophysical factors. The findings indicate that the hierarchical regression approach provides significant advantages in studying migration decisionmaking.
