Year: 2005
Type: article
Abstract: 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.
DOI: 10.68479/001c.9424
Authors: Guangqing Chi, Paul Voss
Institution: University of Wisconsin–Madison
Language: English
Topic: Migration and Labor Dynamics
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
SDG: Reduced inequalities
Open Access status: bronze
OpenAlex ID: W2107283046