Education–occupation mismatch is typically present at the labour market of modern societies. Educational investments and labour market outcomes do not correspond properly and this is general concern from the perspective of graduates as well as for the employers. Nevertheless, the good match is crucial for the assessment and evaluation of higher education, e.g. in terms of country rankings but also at the level of each institution.

The paper investigates the employability of graduates with a focus on mismatch between degree and job. The topic is relevant from the viewpoint of education as expansion in higher education has become a typical feature due to several macro-sociological reasons such as globalisation, an increase in the demand for labour with a higher level of schooling, democratisation or declining state control in higher education (Schofer and Meyer, 2005). This makes the analysis of returns to diplomas particularly appropriate by placing this issue into the broader frame of returns to human capital investments (Mincer 1974. Becker 1975). More precisely, going beyond the wage returns to education, a wage premium for diplomas, graduates may also expect a properly fitting job. Both over-education and under-education lead to the failure of this acceptable requirement.

The topic is relevant from the viewpoint of the labour market because employers search for suitable graduates as candidates to various jobs. The discrepancy between supply and demand in graduate employment is noticeable since universities are frequently unable to provide the right skills and competences, employers would require. Due to the expansion in higher education and modifications in the curricula, signalling functions (Spence 1974) operate to less extent. Rising risks in the labour market make the hunt for proper labour force even more difficult, while job safety as a job characteristic can become more important than the job itself (Sicherman 1991). In this situation, job mismatch may characterise young graduates in the beginning of their career stronger (Brynin 2002).

Education–occupation mismatch is a comprehensive concept. Typical distinctions are vertical mismatch, i.e. working in an occupation which does not require the education, jobholder has; and horizontal mismatch, i.e. working in an occupation which requires a different field of study (Halaby 1994. Robst 2008). The current analysis targets vertical mismatch due to the available data. Another typical distinction is objective and subjective (self-reported) mismatch and previous studies are inconclusive about the reliability of these approaches (van der Velden and van Smoorenburg 1997.  Battu et al., 2000. Groot and Maassen van den Brink 2000. Chevalier 2003. McGuinnes 2006). This study applies an objective approach in line with the available data.

In addition to these measures of job mismatch, the current analysis develops a further concept, definition labelled as relative status mismatch. By this approach the idea is that graduates tend to attain a job with a certain level of prestige score, i.e. job status. In each society, the attained job status has a mean level of prestige but some graduates work in occupation with higher or lower level of prestige score. Accordingly, some graduates work in relatively better jobs with higher prestige (positively mismatched), while other graduates work in relatively worse jobs with lower prestige (negatively mismatched). These notions also correspond to over- and under-employed.

The paper takes a comparative perspective. The 21 countries analysed here represent various country groups (regimes). This allows considering the linkage between universities and employers from the viewpoint of internal vs. occupational labour markets; production vs. training approach; organisational vs. qualificational mobility space, insider vs. outsider labour market, closed and open positions (Maurice et. al. 1986. Marsden 1999. Sorensen and Kalleberg 1981).

The analysis is based on pooled European Social Survey data, Round 7 with 21 countries, the total N of cases is above 30,000. The focus is on the degree holders, roughly a quarter of all observations on average, respondents with BA and MA (N= about 7500). The exact countries and their grouping look like this: Northern EU: Denmark, Finland, Norway, Sweden; Western EU: Austria, Belgium, France, Germany, Netherlands, Switzerland; Anglo-Saxon: Great Britain, Ireland; Southern (Mediterranean): Italy, Portugal, Spain; Post-communist (CEE): Czech Republic, Hungary, Poland, Slovenia; Post-communist (Baltic): Estonia,, Lithuania. For investigating the match between education and occupation, data allow predicting the impact of holding BA or MA degrees but more details, e.g. field of study, are not present. Job is coded by ISCO and this can be transferred into occupational prestige scores (SIOPS – Ganzeboom at al. 1992). For measurement of mismatch two dependent variables are defined. There is an absolute measure, the job does not require a diploma and jobs in ISCO main category 3-9 are considered as under-employment for respondents with tertiary education. Alternatively, a relative country specific measure is also developed. The mean value of the SIOPS score is calculated for graduates with BA and MA degree in each county and the definition of relative mismatch is based on this mean value and its standard deviation. - matching job = SIOPS score around the mean +- 1 std.dev. - positive mismatch = score above the mean + 1 std.dev. & above - negative mismatch = score below the mean - 1 std.dev. & below The paper provides descriptive statistics by countries in comparative manner. Then multivariate analysis is used to investigate the probability of mismatch. For absolute mismatch, logistic regression is applied for predicting the odds of under-employment with tertiary education. For relative mismatch, multinominal logistic regression is applied for predicting positive / negative mismatch (reference: matching job). ESS data cover respondents aged 15 and above but an upper age limit of 65 is defined. Nevertheless, job status used for analysing mismatch refers to the current job and this means an occupation at various ages. Consequently, a control for age / cohort is crucial. In principle, this perspective allows two kinds of interpretation: (1) age effect measures how probability of the mismatch changes as one is getting older; (2) cohort effect measures how probability of the mismatch changes for respondents belonging to younger vs. older cohorts.

The paper is basically an explorative analysis. Job mismatch exists with substantial variation for BA and MA degrees, in the various cohorts and in the various school system – labour market regimes. The rate of absolute mismatch is higher with BA degree, in particular in Portugal, Finland, France, Lithuania, Czech Republic. MA degree saves from under-employment in Denmark, Norway, Switzerland, Portugal to bigger extent. However, the mismatch is relatively high in the UK, Netherlands, Slovenia, Czech Republic, Spain even with MA degree. From a graduate perspective, it is worth to invest into obtaining a MA degree is some countries, while it does not matter to have either a BA or a MA degree in other countries for avoiding under-employment. The occurrence of relative positive mismatch (having a job with higher prestige than the average in the given country) is higher for graduates with MA degree as compared to the occurrence of relative negative mismatch (having a job with lower prestige than the average in the given country). Positive mismatch with MA degree seems to be more frequent in Slovenia, Denmark, Hungary, Finland. BA degree does not show a significant difference. Nevertheless, positive mismatch with BA degree seems to be more frequent in Austria, Germany, Slovenia, Switzerland, Hungary. Graduates belonging to younger cohorts may suffer from education–occupation mismatch to larger extent. This may be due to educational expansion, rising labour market uncertainties or simply because of being in the beginning of their work career. School-to-work transition is getting mixed, conceptual regime characteristics may need refinement and revision. It is getting hard to detect and apply ILM / OLM differences, the role of EPL, and of insider vs. outsider labour market in order to explain or understand country variation.

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