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Skill mismatch among migrant workers: evidence from a large multi-country dataset
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Abstract
This article unravels the migrants’ incidence of skill mismatch taking into consideration different migration flows. Mismatch is the situation in which workers have jobs for which lower skill levels are required compared to their education. We use a dataset (from a large multi-country web survey) particularly suited to investigate differences in skill mismatch between native and migrant workers. The main advantages are its ample size and the large variety of country of origin and destination combinations, which allows for detailed analysis of different migration flows. This provides an innovative multi-country perspective, including nations and migrants from all continents. We also identify the relation between overeducation and some of the most widely accepted theoretical explanations for the phenomenon among native workers and test whether it holds for migrants. These results are achieved by fulfilling three research objectives, which are to investigate (1) the factors affecting overeducation and whether migrants are more often overqualified, (2) the relation between overeducation and different country of origin and destination combinations, and (3) whether a range of theoretically based assumptions affect the incidence of overeducation and the extent to which they are relevant in the case of migrant workers. Skill mismatch is found to be more common among migrants compared to native workers, although the incidence differs across migrants depending on the country of residence. Differences in the incidence of overeducation between native and migrant workers are not only related to the country of residence but also to the combination of country of origin and destination. When theoretically based assumptions are used to explain overeducation, the relation found for the total population does not always hold in the case of migrants. All these findings are confirmed by both an explorative and a in-depth analysis.
Springer Science and Business Media LLC
Title: Skill mismatch among migrant workers: evidence from a large multi-country dataset
Description:
Abstract
This article unravels the migrants’ incidence of skill mismatch taking into consideration different migration flows.
Mismatch is the situation in which workers have jobs for which lower skill levels are required compared to their education.
We use a dataset (from a large multi-country web survey) particularly suited to investigate differences in skill mismatch between native and migrant workers.
The main advantages are its ample size and the large variety of country of origin and destination combinations, which allows for detailed analysis of different migration flows.
This provides an innovative multi-country perspective, including nations and migrants from all continents.
We also identify the relation between overeducation and some of the most widely accepted theoretical explanations for the phenomenon among native workers and test whether it holds for migrants.
These results are achieved by fulfilling three research objectives, which are to investigate (1) the factors affecting overeducation and whether migrants are more often overqualified, (2) the relation between overeducation and different country of origin and destination combinations, and (3) whether a range of theoretically based assumptions affect the incidence of overeducation and the extent to which they are relevant in the case of migrant workers.
Skill mismatch is found to be more common among migrants compared to native workers, although the incidence differs across migrants depending on the country of residence.
Differences in the incidence of overeducation between native and migrant workers are not only related to the country of residence but also to the combination of country of origin and destination.
When theoretically based assumptions are used to explain overeducation, the relation found for the total population does not always hold in the case of migrants.
All these findings are confirmed by both an explorative and a in-depth analysis.
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