Overeducation and Mismatch in the Labor Market
Edwin Leuven () and
Hessel Oosterbeek ()
Chapter Chapter 3 in Handbook of the Economics of Education, 2011, vol. 4, pp 283-326 from Elsevier
This chapter surveys the economics literature on overeducation. The original motivation to study this topic reports that the strong increase in the number of college graduates in the early 1970s in the United States led to a decrease in the returns to college education. We argue that Duncan and Hoffman's augmented wage equationâ€”the workhorse model in the overeducation literatureâ€”in which wages are regressed on years of overschooling, years of required schooling, and years of underschooling is at best loosely related to this original motivation. Next, we discuss how overschooling and underschooling at the level of individual workers have been measured, and what the incidence of overschooling and underschooling is. We then analyze in more detail Duncan and Hoffman's wage equation. We discuss the potential problems with it due to endogeneity and measurement error, and we review the results from earlier studies using this specification. We conclude that because of the issues concerning endogeneity and measurement error, the estimated returns to required/under/overschooling cannot be interpreted as causal.
Keywords: Mismatch; Overschooling; Underschooling; Wage Equation JEL-Classification: I2 (search for similar items in EconPapers)
References: Add references at CitEc
Citations: View citations in EconPapers (198) Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
Working Paper: Overeducation and Mismatch in the Labor Market (2011)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:educhp:4-283
Access Statistics for this chapter
More chapters in Handbook of the Economics of Education from Elsevier
Bibliographic data for series maintained by Catherine Liu ().