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This paper is among the first to investigate the impact of over-education and over-skilling on workers' wages using a unique pan-European database covering twenty-eight countries for the year 2014, namely the CEDEFOP's European Skills and Jobs (ESJ) survey. Overall, the results suggest the existence of a wage penalty associated with over-education. When the educational and the skills mismatches are interacted with each other in order to distinguish apparent over-education from genuine over-education, the results highlight that the workers with the highest wage penalty are those who are both over-educated and over-skilled. [ABSTRACT FROM AUTHOR]

Background Although psychosocial work factors are recognized as major occupational risk factors, little information is available regarding the prevalence of exposure to these factors and the differences in exposure between countries. Aims To explore the differences in various psychosocial work exposures between 31 European countries. Methods The study was based on a sample of 14 881 male and 14 799 female workers from the 2005 European Working Conditions Survey. Eighteen psychosocial work factors were studied: low decision latitude (skill discretion and decision authority), high psychological demands, job strain, low social support, iso-strain, physical violence, sexual harassment, bullying, discrimination, work–family imbalance, long working hours, high effort, job insecurity, low job promotion, low reward and effort–reward imbalance. Covariates were age, number of workers in household, occupation, economic activity, self-employed/employee, public/private sector and part/full time work. Statistical analysis was performed using multilevel logistic regression analysis. Results Significant differences in all psychosocial work factors were observed between countries. The rank of the countries varied according to the exposure considered. However, some countries, especially Denmark, Netherlands and Norway, displayed a significantly lower prevalence of exposure to four factors or more, while some Southern and Eastern countries, especially Czech Republic, Greece, Lithuania and Turkey, had a higher prevalence. Conclusions Differences in psychosocial work exposures were found between countries. This study is the first to compare a large set of psychosocial work exposures between 31 European countries. These findings may be useful to guide prevention policies at European level. [ABSTRACT FROM PUBLISHER]

Objectives To analyse the associations between socioeconomic status (SES), measured using occupation, and self-reported health, and to examine the contribution of various material, occupational and psychosocial factors to social inequalities in health in Europe. Methods This study was based on data from the European Quality of Life Survey (EQLS) carried out in 2003. The total sample consisted of 6038 and 6383 working men and women in 28 countries in Europe (response rates: 30.3-91.2%). Each set of potential material, occupational and psychosocial mediators included between eight and 11 variables. Statistical analysis was performed using multilevel logistic regression analysis. Results Significant social differences were observed for self-reported health, manual workers being more likely to be in poor health (OR=1.89, 95% CI 1.46 to 2.46 for men, OR=2.18, 95% CI 1.71 to 2.77 for women). Strong social gradients were found for almost all potential mediating factors, and almost all displayed significant associations with self-reported health. Social differences in health were substantially reduced after adjustment for material, occupational and psychosocial factors, with material factors playing a major role. The four strongest contributions to reducing these differences were found for material deprivation, social exclusion, financial problems and job reward. Taking all mediators into account led to an explanation of the social differences in health by 78-100% for men and women. Conclusion The association between SES and poor health may be attributed to differential distributions of several dimensions of material, occupational and psychosocial conditions across occupational groups. Interventions targeting different dimensions might result in a reduction in social inequalities in health. [ABSTRACT FROM AUTHOR]