Abstract
Interstate elderly migration has strong implications for state tax policies and health care systems, yet little is known about how it has changed in the twenty-first century. Its relative rarity requires a large data set with which to construct reliable measures, and the replacement of the U.S. Census long form (CLF) with the American Community Survey (ACS) has made such updates difficult. Two commonly used alternative migration data sources—the Current Population Survey (CPS) and the Statistics of Income (SOI) program of the Internal Revenue Service (IRS)—suffer serious limitations in studying the migration of any subpopulation, including the elderly. Our study informs migration research in the post-2000 era by identifying methodological differences between data sources and devising strategies for reconciling the CLF and ACS. Our investigation focusing on the elderly suggests that the ACS can generate comparable migration data that reveal a continuation of previously identified geographic patterns as well as changes unique to the 2000s. However, its changed definition of residence and survey timing leaves us unable to construct a comparable national migration rate, suggesting that one must use national trends in the smaller CPS to investigate whether elderly migration has increased or decreased in the twenty-first century.
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Notes
Ruggles (2013:293) touted the importance of such data as well; while offering advances for migration researchers, the discontinuity in U.S. data caused by the switch to the ACS was not discussed. Similarly, the problems caused by this discontinuity are not addressed in recent work that combines the two sources to study patterns over time (e.g., Iceland et al. 2012).
See also https://www.census.gov/hhes/www/poverty/about/datasources/factsheet.html for a comparison of the survey methodology and population universe of the CPS and ACS, and Willekens (2016) for a more general discussion of the challenges and issues in measuring migration.
The standard error formula is \( \sqrt{p\left(1-p\right)/n} \), where p is the proportion who migrate, and n is the sample size. Across all data sources, the standard error for the total population is approximately one-half the size of the elderly’s, whereas the migration rate (p) is more than twice as large. The full set of calculations is available upon request.
Willekens (2016) pointed out that the population at risk is difficult to define for a net migration rate because the rate captures those moving into the state from other places. Still, one must adjust for population size, and this approach is common.
This measure is similar to that reported by past over-time comparisons and updates of elderly migration (e.g., Flynn et al. 1985; Longino and Bradley 2003) except that they focus on in-migrants and out-migrants separately rather than flows. See also Rogers and Raymer (1998), who discussed alternative measures of spatial focus and recommend the coefficient of variation (CV). Results based on the CV confirm our conclusion regarding concentration.
During any given time interval, individuals can move multiple times such that the number of moves exceeds the observed number of migrants. A longer interval also increases survivor bias because individuals must survive to an older age to be observed (e.g., age 85 vs. 81 for a move made at age 80). This bias is especially acute for the elderly. For both reasons, the number of moves and migrants missed grows with the length of the interval.
Details of these calculations are available upon request.
The CVs for each year (4.7, 3.9, 3.2, and 2.5, respectively) also show a decline in concentration.
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We thank the Editors, referees, and participants in our session at the Population Association of America annual meetings for their helpful comments.
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Conway, K.S., Rork, J.C. How Has Elderly Migration Changed in the Twenty-First Century? What the Data Can—and Cannot—Tell Us. Demography 53, 1011–1025 (2016). https://doi.org/10.1007/s13524-016-0477-7
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DOI: https://doi.org/10.1007/s13524-016-0477-7