Before I answer this question, I will remind readers that the idea of "matching" describes the extent of skills-match between Ph.D. training and subsequent employment (It's a different concept to over-education). Using the National Science Foundation's (NSF) `Survey of Doctorate Recipients' (SDR); Bender and Heywood (2006) report that approximately one-sixth of academics in the United States report some degree of mismatch. This mismatch is associated with substantially lower earnings, lower job satisfaction and a higher rate of turnover (Bender and Heywood, 2006). The question on `matching' in the SDR is collected because the (US) National Research Council made a demand for data that shows the extent of integration between "occupational detail and academic training" i.e. `skills-matching'.
The literature on skills-matching is small; there is at least one study using objective data, and a few more using self-reported data. Nordin, Persson and Rooth (2008) is a study that was already mentioned by me on the blog this week (see here). These authors add to the small literature on the consequences of (objective) skills-matching; they use microdata collected by Statistics Sweden, but are forced to drop 36 percent of their sample due to restrictions on fields of education to well-defined categories. The authors state that this approach is necessary because some fields of education (e.g. in the humanities and languages) are either vague or cannot easily be matched with any specific occupation. Also, the authors exclude a further 11 percent of their sample because of missing occupation data.
Robst (2007) discusses other instances where objective measures of skills-matching may be problematic. For example, "many college majors provide students with a broad range of skills... that apply to different occupations. It would be difficult to develop an algorithm for determining whether a major and a job are unrelated... individual assessments, while perhaps subjective, are expected to provide important information." One way around these problems is to use self-rated measures of skill-matching, augmented by the anchoring vignettes technique (see King et al; 2004: here) for enhancing the comparability of survey responses. I mentioned ongoing work on this here last week.
Showing posts with label over-education. Show all posts
Showing posts with label over-education. Show all posts
Friday, November 14, 2008
Monday, November 10, 2008
Does Skills Mismatch Lower Returns to Education?
A new IZA discussion paper by Nordin, Persson and Rooth (Education-Occupation Mismatch: Is There an Income Penalty?) "adds to the small literature on the consequences of education-occupation mismatches." The consequences of skills-matching and the measurement of skills-matching are issues that have been discussed on this blog a fair bit recently.
Nordin, Persson and Rooth use microdata from Statistics Sweden, but are forced to drop 36 percent of their sample due to restrictions on fields of education to well-defined categories. The authors state that this approach is necessary because some fields of education (e.g. in the humanities and languages) are either vague or cannot easily be matched with any specific occupation. Also, the authors exclude a further 11 percent of their sample because of missing occupation data. One way around these problems might be to use self-rated measures of skill-matching, from which wage penalties might be more accurately estimated.
Nordin, Persson and Rooth focus on the income penalty for field of education-occupation mismatches; they find that the penalty for such mismatches is large for both men and women. They also find that it is substantially larger than has been found for the US. Interestingly, the authors also control for cognitive ability and find that the "income penalty is not caused by ability, at least for Swedish men." The income penalty for men decreases with work experience which the authors suggest is an indication that education-specific skills and work experience are substitutes to some extent. "There is no evidence, though, that the mismatched individuals move to a matching occupation over time. Thus, for some, the income penalty seems to be permanent."
Nordin, Persson and Rooth use microdata from Statistics Sweden, but are forced to drop 36 percent of their sample due to restrictions on fields of education to well-defined categories. The authors state that this approach is necessary because some fields of education (e.g. in the humanities and languages) are either vague or cannot easily be matched with any specific occupation. Also, the authors exclude a further 11 percent of their sample because of missing occupation data. One way around these problems might be to use self-rated measures of skill-matching, from which wage penalties might be more accurately estimated.
Nordin, Persson and Rooth focus on the income penalty for field of education-occupation mismatches; they find that the penalty for such mismatches is large for both men and women. They also find that it is substantially larger than has been found for the US. Interestingly, the authors also control for cognitive ability and find that the "income penalty is not caused by ability, at least for Swedish men." The income penalty for men decreases with work experience which the authors suggest is an indication that education-specific skills and work experience are substitutes to some extent. "There is no evidence, though, that the mismatched individuals move to a matching occupation over time. Thus, for some, the income penalty seems to be permanent."
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