Migration

Most population statistics for states, counties, and cities refer to permanent residents, or persons who spend most of their time in an area. At certain times, however, many states and local areas have large numbers of temporary residents who exert a significant impact on the area's economy, physical environment, and quality of life. Typically, very little is known about the number, timing, and characteristics of these residents.

This article develops three different models of migration for cohort-component projections, each using a different base (i.e., denominator) for migration rates. The differences in the resulting projections are analyzed, and a number of conclusions are drawn regarding the construction of migration rates for use in cohort-component population projections.

The temporary migration of elderly adults has a major impact on the resident populations of both sending and receiving communities. This article presents a methodology for estimating temporary migration and provides insights into migratory patterns that cannot be achieved by focusing solely on changes in place of usual residence.

Most migration statistics in the United States focus on changes in permanent residence, thereby missing temporary moves such as the daily commute to work, business trips, vacations, and seasonal migration. In this paper, we analyze temporary migration streams in Florida, focusing on moves that include an extended stay. Using several types of survey data, we examine the characteristics of non-Floridians who spend part of the year in Florida and Floridians who spend part of the year elsewhere.

Net migration has been widely criticized as a theoretical concept and as a measure of population movement. Many of these criticisms are valid: net migration reflects a residual rather than a true migration process, it often masks large gross migration flows, it cannot account for differences in the characteristics of origin and destination populations, it cannot be used for rates in a probabilistic sense, and it can lead to misspecified causal models and unrealistic population projections.