CEB Working Paper

economic and educational policy as well as for firm behaviour (Mavromaras and
McGuinness, 2012; Quintini, 2011; Sattinger and Hartog, 2013). In a strategy from 2009, the
European Union wants to increase the proportion of individuals with tertiary education in the
age group between 30-34 years to 40% by 2020, a development that requires substantial
investments in education systems. In 2000, 22.4% of the population in EU27 countries was
tertiary educated, a proportion that already increased to 34.6% in 2011 (European
Commission 2012). While formal education is therefore undoubtedly on the rise, figures on
educational mismatch are also high. A report by the European Union from 2012 shows that
between 2001 and 2011 educational mismatch concerned 36% of the workforce in the EU27
countries, albeit with large variations between countries with low incidence (such as Slovakia
with only 16%) and high incidence of over- or undereducated workers (such as Ireland with
51%). The scope of these phenomena marks a stark contrast with the absence of empirical
studies looking at how they affect the bottom line of firms facing a combination of a general
increase in educational norms and a significant share of workers who do not fulfil them.
The remainder of the paper is organised as follows. A review of the literature is presented in
Section 2. Sections 3 and 4 respectively describe our methodology and dataset. Econometric
results are presented in Section 5. The final section discusses the results and concludes.
2. Literature review
2.1. Educational mismatch and productivity-wage gaps
Although labour economists have produced an abundant literature on the link between
educational credentials and wages (Becker 1975, Card 1999), it becomes increasingly
problematic that most empirical studies have relied on the assumption that these credentials
accurately reflect labour productivity. Indeed, the recent literature on wage discrimination has
applied methodological advances – notably an approach pioneered by Hellerstein et al. (1999)
– to extensive matched employer-employee datasets that provide direct measures of labour
productivity. This allowed to re-examine the nexus between worker characteristics, labour
productivity, wages and profits (Cardoso et al., 2011; Damiani et al., 2016; Devicienti et al.,
2015; Konings and Vanormelingen, 2015; Nielen and Schiersch, 2014; van Ours and
Stoeldraijer, 2010, 2011). Instead of merely assuming that wage differentials between
educational groups mirror differences in productivity, which has long been the modus
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