Abstract
The methodological debate on how to measure overeducation has been present since the introduction of the topic in the academic debate. Nevertheless, there is still no consensus on a preferred indicator. This article aims at contributing to the existing methodological debate providing systematic and cross-country evidence on the variation across overeducation measurements. Using REFLEX/HEGESCO and EULFS datasets, I provide evidence on within and across countries variation on the incidence, correlation and overlaps across the main types of objective, statistical and subjective overeducation indicators. Results suggest that worker’s self-reported indicators better cope with comparative studies, while in single-country studies objective indicators are likely to provide a more in-depth and detailed measurement. The use of statistical indicators is advised in contexts with labour markets that easily adapt to educational and employment changes, especially if these are not affected by credential inflation. However, it is advisable to use more than one indicator whenever data allows it, as different types of overeducation measurements provide different outcomes and results are likely to be complementary rather than excluding information on the overeducation phenomenon. This is especially relevant when overeducation is used as a dependent variable rather than a predictor. An initial review and discussion of the existing types of overeducation measurements and their advantages and drawbacks precedes the empirical evidence.
Highlights
Recent higher education expansion among OECD countries has triggered a thriving debate on credentialism and overeducation (Kucel 2011; Quintini 2011; Schofer and Meyer 2005)
It could be argued that JA1 and RM1 indicators are upwardly biased, considering as non-graduate occupations some jobs individuals deem to be adequately matched with their educational level
Sciences and maths and engineering graduates are likely to be overeducated to education graduates (RM1, RM2), some indicators point to slightly larger (WA) or lower (JA1, JA2) likelihood of being overeducated
Summary
Recent higher education expansion among OECD countries has triggered a thriving debate on credentialism and overeducation (Kucel 2011; Quintini 2011; Schofer and Meyer 2005). The estimated incidence of overeducation associated with higher education expansion is around 26% in the U.S and 22% in European countries (Groot and van den Brink 2000). Convergence in overeducation trends are taking place, with rates becoming static or even falling in most European countries (McGuinness et al 2018). These figures are not consistent across all empirical studies. The methodological debate started during the 1980s and it is still ongoing (Battu et al 2000; Chevalier 2003; Groot and van den Brink 2000; Halaby 1994; Hartog 2000; Kucel 2011; McGuinness 2006; Quintini 2011; Verhaest and Omey 2006). Measurement is usually driven by data availability and it is advised to use more than one indicator to cope with the limitations of each type of measurement
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