Temporary and Permanent Migrant Selection

Published By: IFPRI | Published Date: December, 30 , 2015

The migrant selection literature concentrates primarily on spatial patterns. We integrate two workhorses of the labor literature, the Roy and search models, to illustrate the implications of migration duration for patterns of selection. Theory and empirics show that temporary migrants are intermediately selected on education, with weaker selection on cognitive ability. Longer migration episodes lead to stronger positive selection on both education and ability because the associated jobs involve finer employee-employer matching and offer greater returns to experience. Networks are more valuable for permanent migration, where search costs are higher. Labor market frictions explain observed complex network-skill interactions. When considering migrant selection, the economics literature has largely focused on patterns by area of origin. However, the duration of migration episodes–temporary versus permanent–is another important determinant of selection. We integrate two workhorses of the labor literature, the Roy model and a search model, to illustrate the implications of migration duration for patterns of self-selection. We provide theoretical and empirical evidence showing that, because short-term migration episodes have less scope for skill-based matching and greater need for screening, temporary migrants are more likely to display intermediate selection on education, with weaker selection on underlying cognitive ability. Longer term migration episodes, in contrast, allow for finer employee-employer matching and greater returns to experience, leading to stronger positive selection on both education and cognitive ability among permanent migrants. Networks are also found to be more valuable for permanent migration, where search costs tend to be higher. However, we also provide evidence of complex network-skill interactions, driven primarily by labor market frictions.

Author(s): Valerie Mueller, Katrina Kosec, Joyce Chen | Posted on: Jan 07, 2016 | Views()


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