What is it about?
This article focuses on the interrelationship between educational mismatch and earnings, taking three new approaches. First, we examine decompositions of the mismatch wage gap, finding that characteristics explain less than half of the mismatch penalty. Second, we use unconditional quantile regression to examine the mismatch penalty across the earnings distribution, showing that the penalty shrinks as the position in the earnings distribution increases. Third, we decompose the differentials using quantile decompositions. Different reasons for mismatch show heterogeneity in our results, with larger penalties for being mismatched due to working conditions, location, family, and no available job.
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Why is it important?
This paper shows the different gender experiences of educational mismatch and how it impacts wages at different parts of the distribution. It also is one of the first to examine the role of different reasons for mismatch.
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This page is a summary of: Educational mismatch and the earnings distribution, Southern Economic Journal, September 2018, Wiley,
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