Aiming to tackle the high levels of youth unemployment and rates of Not Employed, in Education, or Training (NEET), the European Union launched the flagship policy Youth Guarantee in 2013. In this article we evaluate this policy in order to reveal the lessons it can teach us and possible ways for its improvement to achieve a sustainable active labor market policy. We use the data collected through the Indicator Framework for Monitoring the Youth Guarantee to analyze the policy impact, limited to some of the countries with the highest NEET rates: those of the Mediterranean European Economic Area (Cyprus, Greece, Italy, and Spain). We used the data to create regression models for the evaluation of policy measures, spread, and achievements. In our findings we reveal the importance of time in the policy implementation, the differences and commonalities between the countries, and hidden problems in the data collection that lead to biases and misleading results. We conclude that it is too soon to judge the usefulness of the policy and recommend an improvement in the data collection process.

During the last decade, the policy efforts aiming to tackle the NEET situation (young people who neither study nor work) have increased dramatically in the European Union. Meanwhile, many studies challenged those policies, showing how they failed due to lack of understanding the NEETs. Thus, we analyse the factors that influence the appearance, modification and elimination of a NEET identity. The results reveal the presence of a shared NEET identity in the countries with high NEET rates, despite the considerable differences in the countries’ and NEETs’ characteristics. The NEET rate stands out as the key factor influencing the NEET identity, showing that this identity depends more on economic cycles than on the specific country or its culture.

The rates of young people Not in Education, Employment or Training (NEET) in the Mediterranean European Economic Area (MED EEA) are high. Hence, the European Union and national governments have developed and implemented different policies aimed to tackle the NEET situation. In this article, we try to identify and understand the most relevant barriers that keep vulnerable people as NEETs. We focused on youth as being at the highest risk of becoming NEETs: migrant women aged 25–29. By using semi-structured interviews and focus groups with key stakeholders and NEETs in the Spanish region of Murcia, we collected their views on and experiences with different programs and strategies. We conclude that this group is exposed to additional barriers due to the intersectionality of their characteristics. There are structural and contextual aspects, such as motherhood, a lack of social networks, or not knowing the language, which NEET policies do not address. We highlight the need of (i) improving the adaptiveness of relevant policies by being tailored to specific problems NEETs face, (ii) expanding the focus of NEET policies, and (iii) including the regional perspective in NEET policy design. In our comprehensive approach, we stress that the NEET policy alone cannot solve the NEET problem.

In many cases the label NEET (referring to the youth Not in Education, Employment, or Training) is applied without really taking into account the diversity within the group. Even among the studies focusing on NEET diversity, few publications challenge the use of the label at the international level. This article accepts that challenge and combines the mentioned perspective regarding educational level. The article also presents explanatory models, with variables belonging to four categories (NEETs and unemployment, economy, social aspects and education) aiming to analyze those differences. The results reveal that the difference between countries on the average educational attainment of NEETs and the rest of the youth are considerably big and that (1) variables connecting NEETs and unemployment do not explain those differences; (2) economic variables slightly explain the phenomenon; (3) social aspects show volatile significance; and (4) mostly educational factors clarify the differences. This study shows that for the study countries, the less competitive the education system, the higher risk of becoming a NEET for those who attain higher educational levels.

This chapter of the book is devoted to a thorough presentation and analysis of all available secondary quantitative and statistical data related to NEETs in the four Southern European countries in order to reach a comprehensive understanding of their characteristics. More specifically, the general characteristics of labour markets in all four countries are presented paying special attention to spatial/regional disparities and then the spatial distribution of NEETs in those countries is analysed and discussed. This analysis is based on various indices such as Location Quotient and is accompanied by mapping the spatial distribution of NEETs in all four countries with an emphasis on the target areas in each country. Secondary data analysis is complemented with an analysis of the primary quantitative data which are produced via a questionnaire survey across all target areas in the four countries. Those data concern indicatively issues such as age, gender, type of household, marital status, cohabitation patterns, education and training skills, employment status, time of seeking and not seeking employment, reported reasons of inactivity and so on. Here the issues of gender and immigrant / asylum seeker / refugee status are of great importance as the survey sample is comprised of NEETs that belong to those categories. The overall quantitative analysis raises important questions as regards NEETs and their situation and paves the way for the next chapter which concerns the experiences, interpretations and life strategies of NEETs themselves. <!----> <!----> <!----> <!---->