A Stochastic Learning Model of Migration

Abstract

While the comparative-static model of labor force location is straightforward enough, the theory of the dynamic process of migration is incomplete in that it fails to explain how it is possible for relatively low rates of migration to persist over long periods of time in the presence of a divergence between wage rates (corrected for unemployment) in various localities. Most migration theorists seem to assume that workers take rational account of unemployment rates when they compare the income potentials of alternative locations, but that the great majority of workers irrationally fail to act upon these comparisons once they are made. In this note, a migration theory is developed which relies entirely upon a theory of learning and information transmission, rather than comparative static optimization procedures. It is shown that this leads to the formulation of a migration model which is quite similar to the more intuitively based migration equations which are found in the current literature.