The EU youth guarantee – a critical analysis of its implementation in Spain

The significant increase in youth unemployment in different parts of Europe caused by the economic crisis has led to the European Council Recommendation on Establishing a Youth Guarantee (2013). This Guarantee addresses the so-called ‘NEET’ (Not in Education, Employment, or Training) younger than 25 years of age and stipulates that employment, education or training shall be provided within a period of 4 months. As the initiative is relatively recent, in-depth analyses of its contents and implantation in the Member States are still missing. This paper analyses the European Recommendation and its implementation in Spain, evaluating its potential to improve the situation of the young people and society as a whole and its risks in provoking undesired side-effects. Our results question if the whole target group will be reached and highlight the measures’ low potential to promote a real and durable change. Increasing precariousness and insecurity and the tendency to only redistribute existing labour make it conceivable that the Guarantee may contribute to converting precariousness into a new labour paradigm for the whole population. The insufficient and retroactive funding of the Youth Guarantee can provoke negative side-effects for other social policies and the country's development. We conclude with several recommendations on how to improve the Youth Guarantee and its implementation.     

Likelihood-free parallel tempering

Approximate Bayesian Computational (ABC) methods, or likelihood-free methods, have appeared in the past fifteen years as useful methods to perform Bayesian analysis when the likelihood is analytically or computationally intractable. Several ABC methods have been proposed: MCMC methods have been developed by Marjoram et al. (2003) and by Bortot et al. (2007) for instance, and sequential methods have been proposed among others by Sisson et al. (2007), Beaumont et al. (2009) and Del Moral et al. (2012). Recently, sequential ABC methods have appeared as an alternative to ABC-PMC methods (see for instance McKinley et al., 2009; Sisson et al., 2007). In this paper a new algorithm combining population-based MCMC methods with ABC requirements is proposed, using an analogy with the parallel tempering algorithm (Geyer 1991). Performance is compared with existing ABC algorithms on simulations and on a real example.