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.
Bibliographical noteThanks to the participants of the 2015 New Directions in Human Capital Theory Workshop at the University of Birmingham UK, the 2016 Midwestern Economic Society Annual Meetings, the 2017 Scottish Economic Society Annual Conference, seminar participants at the University of Aberdeen and to the editor and the two referees for helpful comments on the paper. The usual disclaimer applies.
- educational mismatch
- quantile regression