Why Would Subjective Measures of Skills-Matching Be Preferred Over Objective Measures?

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.