Migration decision-making: A hierarchical regression approach

Guangqing Chi, Paul R. Voss

Research output: Contribution to journalArticle

23 Citations (Scopus)

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 decision-making.

Original languageEnglish (US)
Pages (from-to)11-22
Number of pages12
JournalJournal of Regional Analysis and Policy
Volume35
Issue number2
StatePublished - Dec 1 2005

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decision making
migration
regression
census
economics
public

All Science Journal Classification (ASJC) codes

  • Geography, Planning and Development
  • Political Science and International Relations
  • Management, Monitoring, Policy and Law

Cite this

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Migration decision-making : A hierarchical regression approach. / Chi, Guangqing; Voss, Paul R.

In: Journal of Regional Analysis and Policy, Vol. 35, No. 2, 01.12.2005, p. 11-22.

Research output: Contribution to journalArticle

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