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Being NEET in Europe Before and After the Economic Crisis: An Analysis of the Micro and Macro Determinants

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Abstract

In this paper, we analyse the main determinants of the propensity to NEET status in a selection of European countries. The NEET rate is the share of young people, aged between 19 and 30 years, not in Employment, Education or Training. In treating the 19–24 and 25–30 cohorts separately, our hypothesis is that for the younger cohort NEET status is mainly influenced by the school-to-work transition while for the older cohort this status is primarily due to labour market functioning and institutional factors. We apply different specifications of multilevel models with binary outcomes accounting for both personal and macro-economic factors which afford advantages over simple logit models. Estimates refer to 2007 and 2016 in order to verify how the economic crisis changed NEET status. The results confirm our hypothesis, highlighting the crucial role on the NEET propensity of the school-to-work transition in the first approach to the labour market, but also the strong influence of long-term unemployment, confirming the structural nature of the NEET phenomenon especially in countries where NEET levels remain high even during times of economic recovery.

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Fig. 1

Source: OECD (on-line database)

Notes

  1. 1.

    The term NEET was formally introduced in the late 1990 s in the UK government report “Bridging the Gap” in order to identify unemployed 16–18-year-olds who were neither studying nor receiving job training, especially following changes made to unemployment benefits policies (Eurofound 2012; Drakaki et al. 2014). Although the term was initially associated almost exclusively with early school leavers, it currently refers to a heterogeneous category of older people not in education, training or employment for different reasons. We decided to exclude from the analysis 15–18-year-olds—simply termed “young”—because the reasons for the NEET condition in this age class are different and more closely linked to early school leaver status, which requires different types of interventions.

  2. 2.

    According to personal characteristics affecting the status of NEET, many of them are very difficult to observe, such as the propensities to make sacrifices to improve human capital and the unobservable skills. As the NEET condition is the outcome of two distinct processes, that are being out of education and being out of employment, it would be very interesting to analyse the different mechanisms acting on the condition of NEETs. Unfortunately, the data used for the analysis do not allow for this.

  3. 3.

    However, to adequately explain the variability in random coefficients, the number of groups has to be sufficiently high (Heck and Thomas 2000; Rabe-Hesketh and Skrondal 2008). Even if the literature suggests that 20 is a sufficiently large number of groups, we accounted for variability in the random intercept alone—different levels in propensity to NEET status due to country of residence—without investigating each covariate’s different impact across countries. Bryan and Jenkins (2016), who analysed the robustness of estimates in multilevel models, estimated country effects with a sufficient number of countries (i.e., 10–50). They found that though estimates of the parameters associated with fixed predictors resulted in unbiased estimates, the estimates of group-level variances underestimated their true values. The smaller the number of groups, the larger the magnitude of such underestimation.

  4. 4.

    Another caveat in using multilevel models concerns the number of units, since smaller groups exert a smaller impact (Snijders and Berkhof 2008). However, in our study, even if the sample dimension differed considerably across countries, the number of units in each group was always sufficient to guarantee sound estimates.

  5. 5.

    However, ICC should not be grounds for justifying decisions on multilevel models, because its values can be misleading and, particularly in the social sciences, only rarely exceed 10% (e.g. Nezlek 2008). Lastly, the likelihood ratio test compares estimates fitted by maximum likelihood for a simple and a hierarchical model. When the test result is significant, the model with the hierarchical structure is preferred over the simple one.

  6. 6.

    The reason why we have estimated four different multilevel models is linked first of all to the wide range of macro-economic covariates considered. Indeed, as in a stepwise perspective, we preferred to introduce the first time separately homogeneous sets of variables. At a second step, we then estimated the joint effect of all covariates. In this way, we also tested the robustness of results.

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Correspondence to Antonella Rocca.

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Caroleo, F.E., Rocca, A., Mazzocchi, P. et al. Being NEET in Europe Before and After the Economic Crisis: An Analysis of the Micro and Macro Determinants. Soc Indic Res (2020). https://doi.org/10.1007/s11205-020-02270-6

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Keywords

  • Young people
  • NEET rate
  • School-to-work transition
  • Multilevel models with binary outcomes