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Selection, Separation, and Unemployment
Author(s) -
Gönül Şengül
Publication year - 2009
Publication title -
ssrn electronic journal
Language(s) - English
Resource type - Journals
ISSN - 1556-5068
DOI - 10.2139/ssrn.1479208
Subject(s) - separation (statistics) , selection (genetic algorithm) , unemployment , economics , computer science , artificial intelligence , macroeconomics , machine learning
High-skill workers have a lower unemployment rate than their low-skill counterparts. This is because high-skill workers are less likely to become unemployed, not because they are more likely to find a job. This paper proposes an explanation for the skill discrepancy in likelihood of becoming unemployed: high-skill workers are less likely to become unemployed because they are selected more effectively during their hiring process. I use a labor search model with match specific quality to show that skill bias in employee selection practices can account for the differences in job separation probabilities and unemployment rates across skill groups.

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