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In empirical work on population migration, researchers have utilized many different measures of migration. 1 measure that is used periodically is an "an allocation rate", most commonly defined as the number of persons moving from origin i to destination j during the period. While an allocation rate is a valid and interesting measure, it has been misunderstood in several instances, leading to some empirical results that are difficult to interpret. In this paper, previous work on allocation rates is discussed, and 1 study is replicated for the 1975 to 1980 period. It is demonstrated that inclusion of origin variables in such models is theoretically incorrect and results in substantial biases in empirical work. Inclusion of ratios of destination-to-origin variables is valid only with a very narrow interpretation.

"In the literature on population migration, increasing attention has been devoted to quality-of-life factors. Measures of temperature (as proxies for climate) have frequently been employed in empirical work regarding the determinants of population migration decisions. A wide range of such specifications have been used, most of which imply restrictions on the preferences of migrants. In this paper, a variety of temperature variable specifications are analyzed and interpreted. Statistical tests for nested and nonnested hypotheses are used to compare and choose among the various specifications." The model is used to analyze interstate migration in the 48 contiguous states of the United States for the period 1975-1980., (excerpt)

"This study focuses on the specification of distance and space in models of state-to-state population migration [in the United States]....Four models of interstate population migration are developed, with increasingly complex specifications of space. The models are estimated using 1975 to 1980 migration data for the 48 contiguous states of the United States. Common borders and bordering metropolitan areas do influence interstate population flows. Models lacking a more detailed specification of space will lose explanatory power and suffer from biases.", (excerpt)