DYNAMIC MIGRATION MODELS APPLIED TO US LONGITUDINAL DATA

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Our purpose is to develop, formalize, and test a dynamic behavioral model of migration. We propose to breakdown the migration decision-making process into three phases: (a) the decision to search ofr alternative locations; (b) the search for and the evaluation of the alternative locations; and (c) the final decison to move. At each phase of the decison-making process, an individual evaluates his expected costs and benfits before taking action. These costs and benefits (they may be monetary or nonmonetary) depend on the characteristics of the individual, the characteristics of his current location, and the characteristics of the alternative location that he is evaluating. Since these relevant characteristics change over time, the migration decision-making process is a dynamic process. The phases of the decison-making process are unobservable. However, the outcome of this process and the characteristics of the individuals and locations that determine the constituent components of each phase of the process are observable. These pieces of information let us derive a model of migration behavior that can be estimated and tested using longitudinal data. Hypotheses will be tested using two principal sets of data: the Panel Study of Income Dynamics (PSID) and the National Longitudinal Survey of Youth (NLSY). These data contain a wide variety of longitudinal information on individuals pertaining to migration, schooling, marital status, employment, fertility and pregnancy. Thus, these data let us examine not only the effects of background characteristics on migration but also the dynamic associations between migration and behavior in other life domains, such as marriage, child-bearing, child-rearing, schooling, and employment. Both data sets also include information on count of residence, which permits us to supplement the data on individual attributes with information on characteristics of counties using data collected by the U.S. Bureau of the Census and other governmental agencies.