Selective Out-Migration from Florida

Abstract

I test if selective out-migration of unhealthy seniors explains why disability rates are so much lower for Florida, as compared to the national average. This particular area of research is timely given the significant demographic changes relating to aging. Moreover, this study contributes to the body of literature examining migration with respect to disability and widowhood. Using State Federal Information Processing Standard (FIPS) and Public Use Microdata Areas (PUMA), I create national maps showing disability rates for the following age-groups: 50–59, 60–69, and 70+. After creating maps in ARCGIS and conducting univariate and clustering analysis on mobility disability and personal care limitation, I employ multinomial logit (MNL) analysis to test if individuals with disability are more likely to out-migrate from Florida. The regression analyses lend support to the relaxed Litwak and Longino (The Gerontologist, 27(3): 266–272, 1987) second-move hypothesis, which claims individuals with progressively worse health are more likely to undertake another move to be closer to family and friends. I state “relaxed” because the data does not allow one to determine the reason for migration—only that migration occurred during the past year. This research informs policy-makers to recognize that elderly in better health may migrate to places such as Arizona and Florida due to amenity-seeking behavior, but unhealthy elderly are more likely to leave these states due to assistance-seeking behavior. This out-migration can place excess demand on health services for the incoming regions, which requires state and local government to ensure resources are in place. Also noteworthy, my results are less likely to be flawed by erroneous age and sex data in the public use microdata samples (IPUMS) since I stack the 2006 and 2007 American Community Survey (ACS). A recent working studies by Alexander et al. (Inaccurate age and sex data in the Census PUMS files: Evidence and implications. Munich: CESifo, 2010) shows inaccuracies in the IPUMS for the 1 and 5% 2000 Census, the 2003–2006 ACS, the 2005–2007 3-year ACS, and the 2004–2009 current population survey (CPS) files.