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Youth Labor in TransitionInequalities, Mobility, and Policies in Europe$

Jacqueline O'Reilly, Janine Leschke, Renate Ortlieb, Martin Seeleib-Kaiser, and Paola Villa

Print publication date: 2018

Print ISBN-13: 9780190864798

Published to Oxford Scholarship Online: January 2019

DOI: 10.1093/oso/9780190864798.001.0001

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Origins and future of the concept of NEETs in the European policy agenda

Origins and future of the concept of NEETs in the European policy agenda

Chapter:
(p.503) 17 Origins and future of the concept of NEETs in the European policy agenda
Source:
Youth Labor in Transition
Author(s):

Massimiliano Mascherini

Publisher:
Oxford University Press
DOI:10.1093/oso/9780190864798.003.0017

Abstract and Keywords

This chapter describes the origins of the concept of NEETs (young people not in employment, education, or training) and discusses its future role in the European policy agenda by disentangling the heterogeneity of the NEET population. The term NEET entered into European policy debates as an additional indicator to facilitate a better understanding of young people’s vulnerabilities—including young mothers and youth with disabilities—concerning their labor market participation. In order to design policy interventions targeted at the needs of particular subgroups of young people, knowing the size and the characteristics of these subgroups is essential. Using the EU-LFS, this chapter proposes a disaggregation of the NEET indicator into seven subgroups of youth, which enables a better understanding of the composition of the NEET population in Europe and can improve targeting and monitoring of the effectiveness of the policy efforts to (re)integrate NEETs into the labor market or education.

Keywords:   NEET, labor market, youth, European policy agenda, young mothers, disabled youth, reintegration

17.1. Introduction

Deeply concerned about the risk of a “lost generation” and seeking to better understand the complex nature of youth disadvantage, researchers and government officials began to adopt new ways of estimating the prevalence of labor market vulnerability among young people by using the concept of NEETs: young people not in employment, education, or training. Originating in studies carried out in the United Kingdom in the 1980s, the concept was adopted by the European Commission Employment Committee (EMCO), which agreed in 2010 on a definition and the methodology for an indicator to measure and monitor trends in the NEET population of the European Union (EU) as part of the Horizon 2020 strategy.

Once it had entered the European policy debate, the term NEET quickly became a powerful tool for attracting public attention to the multifaceted vulnerabilities of young people and for mobilizing researchers’ and policymakers’ efforts in addressing the problem of labor market participation by young people. The concept of NEETs has since been widely used in the European policy debate: Reducing the number of NEETs is one of the objectives of the European Youth Guarantee and, more recently, prevalence of NEETs has been included as one of the indicators for strengthening the social dimension of the Economic and Monetary Union.

Despite the rapid success of the NEET concept, it is often criticized for its grouping of a highly heterogeneous set of young people under one single term. (p.504) Although the term NEET captures all young people who are in a status of not accumulating human capital through formal channels—namely the labor market or education—this is actually a very diverse population with very different characteristics and needs. The heterogeneity of the NEET population has important consequences for policy responses. Although governments and social partners are rightly setting targets to reduce the overall NEET rate, it is argued here that greater attention should be given to disaggregating the heterogeneous NEET category. Policy interventions sensitive to the needs and barriers faced by particular groups of young people will be more effective than a blanket policy imposed on a heterogeneous group.

This chapter discusses the origin and the future of the NEETs indicator in the European policy framework and proposes a distinction between seven different types within the NEET categorization with a view to better informed targeted policies. First, we examine the origins of the concept of NEETs and how it entered into the European policy debate. This is followed by a critical evaluation of the value added by the concept and of its limitations for policymaking. We then examine the main characteristics of the NEET population in Europe and the risk factors associated with becoming NEET. Finally, a disaggregation of the NEET indicator is proposed and applied to data from the European Union Labour Force Survey (EU-LFS), followed by a discussion of policy implications.

17.2. Origins and evolution of the NEET indicator

The need for an additional indicator able to capture young people who are not in employment, education, or training first emerged in the United Kingdom in the late 1980s as an alternative way of categorizing young people aged 16–17 years. This came about as a result of changes in the UK benefit regime: Specifically, the 1986 Social Security Act and its 1988 implementation withdrew entitlement to Income Support/Supplementary Benefit from young people aged 16–17 years in return for a “youth training guarantee” (Williamson 2010).

As a result of this change and the consequent emergence of this new group, researchers and government officials started to adopt new ways of estimating the prevalence of labor market vulnerability among young people. Williamson (1985) was the first to highlight the emerging crisis of young adulthood. Subsequently, a study of young people in South Glamorgan in Wales (funded by the South Glamorgan Training and Enterprise Council) was the first to produce quantitative estimates of the number of young people aged 16–17 years who were not in education, training, or employment (Istance, Rees, and Williamson 1994). Using more qualitative material, this study also illustrated how some of these young people had arrived at this status, how they were getting by, and what they expected for their futures. Here, Istance and colleagues (1994) used the term Status 0/Status Zer0 (later changed to “Status A”) to refer to a group of (p.505) people aged 16–17 years who were not covered by any of the main categories of labor market status (employment, education, or training). The term Status 0/Status Zer0 was merely a technical term derived from careers service records, where Status 1 referred to young people in post-16 education, Status 2 to those in training, and Status 3 to those in employment. The study concluded with the shocking finding that 16%–23% of the age group in question was in Status Zer0 in the United Kingdom during the 1980s. Without making any claim as to representativeness, Istance and colleagues acknowledged the heterogeneity of the group, depicting different routes into Status Zer0 and different experiences within it. The term Status Zer0 was by no means intended as a negative label; it was more about reflecting societal abandonment of this group. However, the term soon came to represent “a powerful metaphor” for the fact that Status Zer0 young people appeared to “count for nothing and were going nowhere” (Williamson 1997:82). The study captured the media’s imagination (Bunting 1994; McRae 1994), and the term entered into the policy debate in the summer of 1994 as Status A (where A stood for abandoned, as in “the abandoned generation”). In this context, Liberal–Democrat MPs raised questions about the Status A phenomenon in Parliament and convened a debate in the House of Lords (Williamson 2010).

Against this background, the term NEET was coined in March 1996 by a senior Home Office civil servant who had detected resistance on the part of policymakers working with the original and often controversial terms of Status Zer0 and Status A. Embracing the concept previously introduced by Istance et al. (1994), the term NEET replaced the other labels and was then formally introduced at the political level in the United Kingdom in 1999 with the publication of the government’s Bridging the Gap report from the Social Exclusion Unit of the New Labour government (SEU 1999).

The term NEET rapidly gained importance outside the United Kingdom, too. By the beginning of the new millennium, similar definitions had been adopted in almost all EU member states; similar concepts referring to disengaged youth were also emerging in popular discourse in Japan, New Zealand, Taiwan, Hong Kong, and—most recently—China (Mizanur Rahman 2006; Liang 2009; Eurofound 2012; Pacheco and Dye 2013). Some of these new concepts went beyond the original meaning of NEET, also attaching a negative stigma to these newly identifiable categories of youth. For example, hikikomori in Japan means “withdrawal” and is used to refer to young Japanese NEETs, usually young men, who live with their parents, spend their time alone in their rooms, are without friends, and engage only in activities on the Internet or in watching movies (Jones 2006; Wang 2015). In Spain, the term generación ni-ni became popular before the crisis as a means to identify young people who did not want to grow up by studying or going to work (Navarrete Moreno 2011); similar terms with negative connotations were also used in Italy (bamboccioni) and Germany (Nesthocker)—usually for young men who appeared unwilling to leave home and “grow up.” Thus, although it had (p.506) originated in the United Kingdom, the concept of NEETs was gradually being recognized in a number of other economically advanced countries.

17.2.1. NEETs at the European level

As the term became more popular across Europe, “NEETs” came to refer to young people aged 15–24 or 15–29 years who were not in employment, education, or training, and it was measured and mapped using national labor force surveys. Nevertheless, this seemingly simple definition masks considerable diversity between countries with regard to the characteristics of the young people classified as NEET. In the UK context, NEETs were frequently associated with problematic labor market transitions. In other countries—with well-functioning transmission paths into education and employment—NEETs were not present and youth transitions were not problematized in the same manner (Wallace and Bendit 2009; Filandri, Nazio, and O’Reilly, this volume).

The totality of those classified as NEETs can also include a diversity of experiences ranging from unemployed graduates taking their time to find work to unqualified early school-leavers and those taking on family caring responsibilities. Some of this diversity has been captured in a number of studies from the Organization for Economic Co-operation and Development and the European Commission (Walther and Pohl 2005; Carcillo et al. 2015). A study by Eurofound (2012) provided the first comparative analysis of the extent of the NEET phenomenon in Europe, examining the economic and societal costs of not integrating youth into the labor market.

At the European policymaking level, EMCO and its Indicators Group (European Commission, DG EMPL) agreed on a definition and a methodology for a standardized indicator to measure and compare the NEET population in Europe as part of its monitoring of the Europe 2020 strategy in April 2010 (European Commission 2011a, 2011b). The definition of NEETs implemented by Eurostat refers to young people aged 15–24 years who are unemployed or inactive according to the International Labour Organization (ILO) definition1 and who are not in any form of education or training.

The Eurostat definition of NEET is constructed as follows: The numerator of the indicator refers to persons who are not employed (i.e., unemployed or inactive) and/or have not received any education or training during the 4 weeks preceding the survey; the denominator consists of the total population of the same age and gender. The NEET indicator is calculated using cross-sectional data from the EU-LFS, observing established rules for statistical quality and reliability (European Commission 2010b, 2011a).

The main NEET indicator produced by Eurostat covers various age groups. For analytical purposes, and given a conceptualization of youth as an age group that varies substantially across different countries (Wallace and Bendit 2009), the indicator is then disaggregated by gender and is available for different age groups (15–17/15–19/15–24/15–29/15–34/18–24/20–24/20–34/25–29 years).

(p.507) Breakdowns by labor market status (unemployed and inactive) and education level (at most lower secondary attainment/at least upper secondary attainment) are also available on the Eurostat website (European Commission 2011a).

The NEET indicator is constructed each year using the EU-LFS according to the following equation:

NEETRate=Numberofyoungpeoplenotinemployment,education,ortrainingTotalpopulationofyoungpeople

The NEET indicator thus measures the share of young people who are not in employment, education, or training among the total youth population. This is not the same as the youth unemployment rate, which measures the share of young people who are unemployed among the population of young people who are economically active (i.e., employed or searching for work, and excluding students). For this reason, although the youth unemployment rate is generally higher than the NEET rate, in absolute terms, the overall number of NEETs is generally higher than the overall number of young unemployed people (Figure 17.1). For example, although in 2015 the youth unemployment and NEET rates in Europe were 20.3% and 12%, respectively, the population of unemployed youth accounted for 4,640,000 individuals, whereas the population of NEETs was 6,604,000 individuals. (p.508)

Origins and future of the concept of NEETs in the European policy agenda

Figure 17.1 Unemployment compared to NEET.

Source: Eurofound (2012).

17.2.2. NEETs in the European policy agenda

Once a standardized definition had been agreed and operationalized at the EU level, the term NEET became increasingly central to the European policy agenda: NEETs were explicitly targeted for the first time in the Europe 2020 flagship initiative Youth on the Move (European Commission 2010a). The initiative states its mission as “unleashing all young people’s potential,” and emphasizes the importance of reducing the “astonishingly” high number of NEETs in Europe by providing pathways back into education or training and by enabling contact with the labor market. Most important, and going beyond youth unemployment, the initiative places special emphasis on ensuring the labor market integration of young people with disabilities or health problems.

Building on Youth on the Move, NEETs consequently became central to the new set of integrated guidelines for economic and employment policies. In 2011, the Youth Opportunities Initiative drew attention to the increasing share of young people not in employment, education, or training (European Commission 2011a), proposing a combination of concrete actions by member states and the EU to tackle the issue (Hadjivassiliou et al., this volume; Petmesidou and González Menéndez, this volume).

By 2012, several documents drawn up as part of the employment package Towards a Job-rich Recovery (European Commission 2012) emphasized the importance of tackling the NEET crisis and suggested making greater use of the European Social Fund for the next program period (2014–2020). One proposal was to make the sustainable integration of NEETs into the labor market (through youth guarantees and other measures) one of the investment priorities for the new program period. NEETs were identified as the most problematic group in terms of labor market trends and challenges (European Commission 2012).

Against this background, NEETs are at the heart of the Youth Guarantee, which aims to reduce NEET rates by ensuring that all young people aged 15–24 years not in employment, education, or training receive a good-quality offer of employment, continued education, or an apprenticeship or traineeship within 4 months of becoming unemployed or leaving formal education. Following a long debate starting in 2005, the Youth Guarantee was proposed by the European Commission in December 2012 and endorsed by the Council of the European Union on April 23, 2013 (Council of the European Union 2013). To make the practical implementation of the Youth Guarantee a reality, the European Commission published the Youth Employment Initiative (YEI), supported by €6 billion of funding, which targeted young NEETs (European Commission 2013a, 2013b).

Furthermore, NEETs are now regularly referred to in the documents of the European Employment, Social Policy, Health and Consumer Affairs Council, and the topic of NEETs has been a priority for recent European Council presidencies. (p.509) In the first half of 2013, the Irish Council presidency focused extensively on youth unemployment; in fact, it was during this period that the establishment of the Youth Guarantee was recommended. Subsequent presidencies frequently referred to the situation of NEETs (Council of the European Union 2013, 2014, 2015). Similarly, the European Parliament also took on board the NEET concept, as in a recent briefing on the youth employment situation in Greece (European Parliament 2015), but also in more generic publications examining the social situation in the EU (European Parliament 2014). When the pre-financing of the YEI was discussed in 2015, the NEET indicator played an important role in policy formulations.

17.3. Value added and limitations of NEET as a concept for policymaking

As with every new concept entering the policy debate, the NEET concept has often struggled to be understood in terms of what exactly it is and what it was designed to do. NEET and youth unemployment are related concepts, but there are important differences between the two. NEET goes beyond unemployment in that it captures all young people who, for various reasons, are unemployed or inactive and are not accumulating human capital through formal channels (Eurofound 2012, 2016).

Although the NEET indicator is easily defined and captures a very general and heterogeneous population of all young people who—regardless of their education level and sociodemographic characteristics—are not in employment, education, or training, the term is sometimes used as a shortcut to identify solely the most vulnerable and the population most at risk of being socially excluded. The misuse of the NEET acronym can probably be traced back to the origins of the concept in the United Kingdom: Being NEET was more closely associated with early school-leaving and other severe patterns of vulnerability that lead to a higher risk of social exclusion and a lack of employment.

However, today this correspondence between risk of social exclusion and NEET status is far from being univocal. By enlarging the age category to the 15- to 24-year-old age group (or even to 15- to 29-year-olds), NEET captures all young people who are not currently participating in the labor market or in education. This includes vulnerable groups and those with accumulated disadvantages (including lower education levels, immigration background, health issues, young mothers, or young people with a difficult family background). But it also includes more privileged youth who voluntarily become NEET—while waiting for a particular opportunity or while attempting to pursue alternative careers (see Filandri et al., this volume; Zuccotti and O’Reilly, this volume). The heterogeneity of this (p.510) group means that the concept of NEET, when applied to the older youth cohort, no longer provides the same shortcut to identify the most vulnerable youth. In addition, there is a negative association in the media and public discourse in which NEET implies that young people do not want to work or study (Serracant 2013); this has been particularly true in some of the public discourse preceding the financial crisis.

The concept of NEET has been adopted in very different ways by governments and international organizations (Elder 2015). NEET is often associated with issues of joblessness, discouragement, or marginalization of youth, but it cannot be equated only with one of these areas; rather, it lies at the intersection of the three issues. The Eurofound (2012) study strongly related NEET to a lack of human capital accumulation through formal channels, whereas Elder (2015) concludes that the best interpretation of the term goes beyond a “productivist” approach and that the best fit is offered by marginalization/exclusion/disaffection. Williamson, who coined the concept under the name Status Zer0 (subsequently changed to NEET), rejects the use of the term “disaffection” to characterize NEETs, arguing for language that is less judgmental; hence his advocacy of “disengagement” or “exclusion,” which in turn allow for re-engagement and inclusion (Williamson 2010).

Despite the relative novelty of the NEET concept, it has had a strong catalyzing effect in attracting and mobilizing policymakers and public opinion. As well as having entered the youth policies lexicon, the concept of NEET is now highly popular among European media. Given the country’s high share of NEETs, Italian media, for example, have defined Italy as the nation of NEETs (Corriere della Sera 2015; L’Espresso 2015). Similarly, in the United Kingdom, the BBC has repeatedly called for greater attention to be paid to the situation of NEETs (BBC 2012, 2014), while the Spanish newspaper El País has described the apathy and passiveness of NEETs and their general situation (El País 2014, 2015). The NEET concept has the capability to increase the understanding of the various vulnerabilities of young people by placing particular groups such as the low educated, early school dropouts, young mothers, or young people with disabilities at the center of policy debates. These groups would otherwise simply be classified as inactive, usually with very limited attention being dedicated to them from a policy perspective (see Berloffa, Matteazzi, and Villa, this volume). Making the reduction of the NEET rate a policy target, as the Youth Guarantee does, means preparing policies to reintegrate young people into education and the labor market that go beyond the issue of unemployment and the needs of the conventionally unemployed. Although there is no doubt that policy focused on reducing NEET rates is important, recognition of the heterogeneity of this group requires tailored policy interventions (Furlong 2007; Eurofound 2012).

(p.511) 17.4. The characteristics of NEETs in Europe

Despite its limitations, the standardized indicator proposed by EMCO and operationalized by Eurostat in 2010 makes it possible to estimate the number of young people who are disengaged from the labor market and from education in Europe and to perform cross-country comparisons on the basis of the usual socioeconomic variables (Eurofound 2012, 2016).

According to the latest Eurostat data, the share of young people aged 15–29 years in Europe who were not in employment, education, or training was 14.8% in 2015. In absolute numbers, this corresponds to approximately 13 million young people belonging to the NEET group. As shown in Figure 17.2, the prevalence of NEETs varies substantially across member states. The Netherlands, Sweden, Luxembourg, and Denmark record the lowest NEET rates (approximately 7%). Croatia, Romania, Bulgaria, Greece, and Italy record the highest rates (greater than 20%), which implies that at least one out of five young people in these countries is not in employment, education, or training. In absolute terms, the NEET population is highest in Italy, with more than 2 million young people belonging to this group.

Origins and future of the concept of NEETs in the European policy agenda

Figure 17.2 NEET rates across Europe (young people aged 15–29 years).

Source: Eurostat (EU-LFS).

Before the economic crisis of 2008–2009, NEET rates were decreasing across Europe: The lowest level of NEETs was recorded for all age categories in 2008. However, with the beginning of the economic crisis, this improvement ended abruptly, and NEET rates increased markedly. European NEET rates were at (p.512) their highest in 2013, when 15.9% of young people aged 15–29 years were NEET, compared to 13% in 2008. NEET rates have now started to decrease slowly, falling to below 15% in 2015 for those aged 15–29 years.

In terms of socioeconomic characteristics, analysis of the EU-LFS reveals considerable heterogeneity across member states. At the European level, there are more female than male NEETs. In the age category 15–29 years, the female NEET rate was 16.7% at the European level in 2015, compared to 13% for males. This gap of 3.7% constitutes a considerable reduction compared to the 6% recorded in the precrisis period. Although considerable gender variability is found at the member-state level, only in Luxembourg, Cyprus, Croatia, and Finland is the share of young males higher than that of young women among NEETs. Conversely, the gender NEET gap is larger in the United Kingdom, Germany, Malta, Hungary, and the Czech Republic, where the great majority of NEETs in this age category are young women.

In terms of education, at the European level, 39% of young NEETs (aged 15–29 years) have a lower education level, 47% have an upper secondary level of education, and 14% have tertiary education. Substantial heterogeneity is observed across member states with regard to educational attainment. In countries such as Spain, Malta, and Germany, more than 50% of NEETs have a low education level. Conversely, in Poland, Greece, and Croatia, more than 60% of NEETs hold an upper secondary diploma. Finally, in Cyprus, more than 30% of NEETs have completed tertiary education. Furthermore, the disaggregation of upper education levels between general courses and vocational education and training (VET) reveals that the group of NEETs with a VET-oriented upper education level is larger than those with more general qualifications.

17.5. Risk factors for becoming NEET: Disadvantage and disaffection

As reviewed in the Eurofound (2012) study, there is reasonable agreement in the literature about the range of social, economic, and personal factors that increase the chances that an individual might become NEET, and it is generally perceived that the NEET status arises from a complex interplay of institutional, structural, and individual factors (Hodkinson 1996; Hodkinson and Sparkes 1997; Bynner 2005; Eurofound 2012).

Focusing on the vulnerable groups (i.e., involuntary NEETs), the literature suggests that there are two principal risk factors relating to NEET: disadvantage and disaffection. Whereas educational disadvantage is associated with social factors such as the family, school, and personal characteristics, disaffection concerns the attitudes young people have toward education and schooling specifically, as expressed by truancy or behavior that leads to expulsion from school. (p.513) There also seems to be a clear correlation between both educational disadvantage and disaffection prior to age 16 years and later disengagement (SEU 1999). Both educational disadvantage and disaffection are linked to a number of background factors, such as family disadvantage and poverty; having an unemployed parent(s); living in an area with high unemployment; membership in an ethnic minority group; or having a chronic illness, disability, and/or special education needs (Coles et al. 2002; see also Berloffa, Matteazzi, and Villa, this volume; Zuccotti and O’Reilly, this volume).

Although it should be emphasized that it is often not easy to differentiate between those factors that cause or lead to NEET status and those factors that are simply correlated with being NEET (Farrington and Welsh 2003, 2007), existing research places great emphasis on family background and individual characteristics as determinants of the NEET status (Stoneman and Thiel 2010). At the individual level, characteristics that are over-represented among the NEET population are low academic attainment (Dolton et al. 1999; Meadows 2001; Coles et al. 2002); teenage pregnancy and lone parenthood (Morash and Rucker 1989; Cusworth et al. 2009); special education needs and learning difficulties (Cassen and Kingdon 2007; Social Exclusion Task Force 2008); health problems and mental illness (Meadows 2001); involvement in criminal activities; and low motivation and aspiration, including lack of confidence, sense of fatalism, and low self-esteem (Strelitz and Darton 2003). Moreover, motivation is often identified as one of the key factors among the nonvulnerable who may be in a “voluntary NEET status”—that is, those who are more likely to come from a privileged background and remain briefly outside the labor market and education in order to sample jobs and educational courses (Furlong et al. 2003; Pemberton 2008).

In order to perform a pan-European investigation of the NEET phenomenon in this chapter, the Eurostat definition of NEET is implemented in the European Values Study survey (EVS), focusing on young people aged 15–29 years. The EVS is a large-scale, cross-national, and longitudinal survey research program on basic human values, which provides insights into the ideas, beliefs, preferences, attitudes, values, and opinions of citizens for 47 European countries and regions. It is an important source of data for investigating how Europeans think about life, family, work, religion, politics, and society, and specific attention is dedicated to individual socioeconomic and family-related variables. On this basis, we explore the characteristics of NEETs in Europe by making use of the set of key characteristics identified in the literature, which includes, especially, the investigation of individual and family background characteristics. In particular, in our analysis, we use the 2008 wave (the most recent) of the EVS by considering data from all 27 EU member states, with an overall sample of more than 40,000 observations that are representative for the EU population. NEETs are identified in the EVS as those young people aged 15–29 years who declared not being in paid employment because of being unemployed, disabled, young carers, housewives, or not otherwise employed for undeclared reasons. This operationalization of the (p.514) definition of NEET is equivalent to that implemented by Eurostat using the EU-LFS, and the computed rates are comparable. Data refer to 2008, so they capture the scenario only at the beginning of the crisis.

The characteristics of the NEETs in Europe have been investigated using a logit model that accounts for a broad set of individuals’ sociodemographic and family-related variables while also controlling for countries’ heterogeneity. We investigated a large set of individual characteristics: gender, age, immigration background, perceived health status, education level, religiosity, and living with parents. Furthermore, at the family level, we considered household income, education level of parents, unemployment history of parents, and the area where the household is located. The analysis is performed at the European and also at the cluster level, which are identified on the basis of the extent of the NEET phenomenon observed at country level and the mediating role of different welfare-state models (Marshall 1950; Hadjivassiliou et al., this volume). In this respect, the established categorization of member states in five clusters is adopted here: employment-centered (AT, BE, DE, FR, LU, and NL), universalistic (DK, FI, and SE), liberal (IE and UK), subprotective (CY, ES, GR, IT, MT, and PT), and post-socialist (BG, CZ, EE, HU, LT, LV, PL, RO, SI, and SK). The results of our analysis show a high level of consistency with the general literature and reveal some heterogeneity among the risk factors observed in the different geographical clusters. In particular, the findings indicate that the probability of ending up NEET is influenced by the following factors and characteristics (Table 17.1):

  • Regarding gender, young women are more likely than men to be NEET. The interpretation of the odds ratio shows that because of family responsibilities, young European women are 62% more likely than men to be NEET. Interestingly, this effect is stronger in the subprotective and post-socialist clusters than in the universalistic, liberal, or employment-centered clusters.

  • As indicated in the literature, those perceiving their health status as bad or very bad and who are suffering from some kind of disability are 38% more likely to be NEET compared to those with a good health status. This effect is stronger in the liberal and universalistic clusters than in the rest of Europe.

  • Young people with an immigration background are 68% more likely to become NEET compared to nationals. This effect is strongest in the liberal cluster, whereas it is not significant in the universalistic or in the subprotective cluster.

  • Young people living in a partnership are 67% more likely to be NEET compared to those living alone or with parents. This effect is mainly driven by young women with family responsibilities. It is strongest in the liberal, subprotective, and post-socialist clusters, whereas it is not significant elsewhere. (p.515)

  • (p.516) (p.517) Education is the main driver affecting the probability of being NEET: Young people with lower level education are two times more likely to be NEET compared to those with secondary education, and they are more than three times more likely to be NEET compared to those with tertiary education. The effect of education is strongest in the liberal cluster, whereas it is very limited in the subprotective cluster.

  • Capturing both the heterogeneity of the NEET population and its composition (both vulnerable and nonvulnerable youth), the marginal effect of income emerges as a U-shaped curve. The probability of being NEET is higher for those with a lower income, then decreases for the middle-level income, and increases again for higher incomes. Again, the effect of income is strongest in the liberal cluster, whereas it is more limited in the subprotective and universalistic clusters.

In addition to these individual characteristics, the following intergenerational influences and family backgrounds play a significant role in increasing the probability of being NEET:

  • Having parents who experienced unemployment is not significant at the EU level, whereas it is only marginally significant in the subprotective cluster.

  • Those with parents with a low level of education are up to 50% more likely to be NEET compared to young people with parents with a secondary level of education, and they are up to twice as likely to be NEET compared to those with parents with a tertiary level of education. This effect is strongest in the liberal cluster, whereas it is not significant in the universalistic cluster.

  • Young people who experienced the divorce of their parents are almost 30% more likely to be NEET compared to those who did not.

Despite some heterogeneity at the cluster level, the results of the investigation indicate that NEET status can be described as both an outcome and a defining characteristic of disadvantaged youth, who are at much greater risk of social exclusion. Education is the most important variable, and it has the strongest effect in influencing the probability of being NEET: This is true at both the individual level and the family level and in all clusters considered. Moreover, suffering some kind of disadvantage, such as a disability or having an immigration background, strongly increases the probability of being NEET, and this effect is strongest in the liberal cluster (Zuccotti and O’Reilly in this volume suggest that these effects also vary by ethnic group and appear to diminish somewhat for second-generation migrants). The importance of family background is confirmed as increasing the risk of becoming NEET. In particular, young people with a difficult family background, such as those with divorced parents or with (p.518) parents who have experienced unemployment, are more likely to be NEET (as in the subprotective cluster) (see Berloffa, Matteazzi, and Villa, this volume). The heterogeneity of the NEET population, as a mix of vulnerable and nonvulnerable situations, is, however, confirmed by the effect of income, which is common to all clusters but the universalistic.

Table 17.1 Logistic regression results

Variable

European Union

Cluster 1: AT, BE, DE, FR, LU, NL

Cluster 2: DK, FI, SE

Cluster 3: IE, UK

Cluster 4: CY, ES, EL, IT, MT, PT

Cluster 5: BG, CZ, EE, HU, LT, LV, PL, RO, SI, SK

Odds ratio

SE

Odds ratio

SE

Odds ratio

SE

Odds ratio

SE

Odds ratio

SE

Odds ratio

SE

Gender (male)

0.381***

0.034

0.615***

0.116

0.399***

0.137

0.111***

0.069

0.393***

0.080

0.289***

0.040

Age (years)

1.066***

0.015

1.118***

0.037

0.993

0.062

0.997

0.099

1.053*

0.033

1.073***

0.024

Health (not good)

1.388***

0.159

1.938***

0.475

2.580**

0.995

3.175*

2.105

2.149***

0.624

0.930

0.160

Immigration background

1.689***

0.261

1.969**

0.529

1.621

0.993

8.965***

6.431

1.287

0.390

2.803***

0.970

Living with parents (ref.)

(dropped)

(dropped)

(dropped)

(dropped)

(dropped)

(dropped)

Living alone

0.804

0.114

0.703

0.204

0.754

0.436

2.185

1.882

0.755

0.220

0.723

0.187

Living with partner

1.673***

0.183

1.057

0.268

1.402

0.711

4.248*

3.634

1.621*

0.405

2.051***

0.317

Experienced divorce

1.265**

0.142

1.338

0.283

1.677

0.572

1.353

0.877

1.499

0.479

1.044

0.188

Education level: primary (ref.)

(dropped)

(dropped)

(dropped)

(dropped)

(dropped)

(dropped)

Education level: secondary

0.448***

0.048

0.452***

0.105

0.514

0.247

0.151***

0.098

0.754

0.186

0.375***

0.061

Education level: tertiary

0.320***

0.048

0.148***

0.055

0.490

0.307

0.183**

0.135

0.568*

0.191

0.321***

0.072

Income

0.443***

0.042

0.356***

0.084

3.395

2.751

0.112***

0.079

0.683

0.165

0.391***

0.063

Income squared

1.051***

0.013

1.094**

0.043

0.603**

0.153

1.332***

0.123

0.997

0.042

1.056***

0.018

Highest education parents: primary (ref.)

(dropped)

(dropped)

(dropped)

(dropped)

(dropped)

(dropped)

Highest education parents: secondary

0.656***

0.071

0.626**

0.146

1.007

0.445

0.104**

0.099

0.618**

0.151

0.646***

0.107

Highest education parents: tertiary

0.524***

0.079

0.531**

0.158

1.338

0.640

0.323

0.257

0.353**

0.156

0.527**

0.131

Unemployment history (father)

1.199

0.223

0.832

0.357

0.428

0.462

1.912

1.582

2.504*

1.238

1.113

0.301

Country dummies

Omitted

Omitted

Omitted

Omitted

Omitted

Omitted

No. of observations

4,470

1,259

344

156

779

1,933

Pseudo R2

0.168

0.194

0.169

0.42

0.135

0.198

17.6. Policies to tackle the heterogeneity of NEETs

Understanding the composition of the NEET population is essential for policy design and for implementing reintegration measures. Armed with information about the size and the characteristics of each subgroup of the NEET population, member states can also better understand how to prioritize their actions and know which tools are most needed in order to reintegrate young people into the labor market or education.

Several alternative theoretical categorizations of NEETs have already been proposed in the literature. Williamson (1997) suggested disaggregating NEETs into three groups: “essentially confused,” “temporarily side-tracked,” and “deeply alienated.” According to Williamson, whereas members of the first group are willing and ready to re-engage as long as the right support and encouragement are provided, those in the second group need some understanding and patience while they deal with what they consider to be more important matters in their lives right now. Williamson considers the third group to be at the highest risk of disengagement and disaffection. This group may include those who have discovered “alternative ways of living” within the informal and illegal economies and those whose lives revolve around the consumption of alcohol and illegal drugs. Although it would be possible to re-engage the “temporarily side-tracked” and the “essentially confused” into the labor market or education, it could be very difficult to persuade the “deeply alienated” to return.

An alternative categorization has been developed by Eurofound (2012, 2016) and Mascherini (2017), who identified five categories within the NEET population with varying degrees of vulnerability and needs: the conventionally unemployed, the unavailable, the disengaged, opportunity seekers, and voluntary NEETs. The “conventionally unemployed” were expected to be the largest group within the NEET population, which could be further divided into short- and long-term unemployed. The “unavailable” include young people who are unavailable because of family responsibilities or because of illness or disability. The “disengaged” include all young people who are not seeking a job or following any education or training and who do not have other obligations that stop them from doing so. This category includes discouraged workers and young people who are pursuing dangerous and asocial lifestyles. The “opportunity seekers” include young people who are seeking work or training but are holding out for the right (p.519) opportunity. The “voluntary NEETs” are constructively engaged in other activities, such as art, music, or self-directed learning.

Although the previous categorizations are quite rich, their implementation is rather difficult because of data constraints that do not allow their operationalization through the EU-LFS, the survey officially used to compute the NEET rate. The EU-LFS has the undoubted advantage of having the largest sample base of any European survey, but it offers a restricted number of variables. This makes it difficult to capture the sociodemographic qualities and behaviors that are essential to a better understanding of the characteristics of NEETs, the reasons for their status, and their vulnerabilities. The limited range of variables also makes it impossible to use the previously described categorizations of vulnerable and nonvulnerable NEETs because the variables that would capture these characteristics are missing.

Building on findings from previous research and using the EU-LFS, a new categorization is proposed here. This categorization revolves around seven descriptions created using the available five variables that make it possible to understand why those in each particular group responded during the survey that they were not searching for employment and were not able to start work within the next 2 weeks.2 Similarly, duration of unemployment has been used to disaggregate the short- and long-term unemployed.

The seven subcategories that emerged from this exercise are as follows:

  • Re-entrants: This category captures those young people who will soon re-enter employment, education, or training and will soon begin or resume accumulation of human capital through formal channels. They are people who have already been hired or have enrolled in education or training and will soon start this activity.3

  • Short-term unemployed: This category is composed of all young people who are unemployed, seeking work, and available to start within 2 weeks and who have been unemployed for less than 1 year.4

  • Long-term unemployed: This category is composed of all young people who are unemployed, seeking work, and available to start within 2 weeks and who have been unemployed for more than 1 year. People in this category are at high risk of disengagement and social exclusion.5

  • Unavailable because of illness or disability: This category includes all young people who are not seeking employment or are not available to start a job within 2 weeks because of illness or disability. This group includes those who need more social support because the nature of their illness or disability means they cannot carry out paid work.6

  • Unavailable because of family responsibilities: This group includes those who are not seeking work or who are not available to start a new job because they are caring for children or incapacitated adults or have other less specific family responsibilities. Young people in this group (p.520) are a mix of the vulnerable and nonvulnerable; some are not able to participate in the labor market because they cannot afford to pay for care for their child or adult family member, whereas others voluntarily withdraw from the labor market or education to take up family responsibilities.7

  • Discouraged workers: This group encompasses all young people who have stopped searching for work because they believe that there are no job opportunities for them. They are mostly vulnerable young people at high risk of social exclusion who are very likely to experience poor employment outcomes over the course of their working lives and are at high risk of lifelong disengagement.8

  • Other inactive: This group contains all NEETs whose reasons for being NEET do not fall into any of the previous six categories. This group is a statistical residual category made up of those who did not specify any reason for their NEET status. It is likely to be an extremely heterogeneous mix that includes people at all extremes of the spectrum of vulnerability: the most vulnerable, the difficult to reach, those at risk of being deeply alienated, the most privileged, and those who are holding out for a specific opportunity or who are following alternative paths.9

The proposed categorization allows investigation of the composition of the NEET population by identifying seven major groups, four of which are labor-market driven (re-entrants, short-term unemployed, long-term unemployed, and discouraged workers), whereas three are inactivity driven (unavailable because of illness or disability, unavailable because of family responsibilities, and other inactive). Although the categorization is not exhaustive, it can be implemented every year through the EU-LFS, providing a useful tool for measuring the extent of NEET populations and the broad types of policy initiative among the various EU member states, showing not only the heterogeneity of the NEET population but also the heterogeneity of the member states, where NEET status differs in terms of not only rate but also composition.

17.6.1. Differentiating the composition of the NEET population and appropriate policy responses

Focusing on young people aged 15–29 years, we implemented the categorization outlined previously on data from the 2013 EU-LFS.10 Figure 17.3 thus shows that in 2013, the largest category of NEETs was the short-term unemployed (25.5%), followed by the long-term unemployed (23.1%). The group of those unavailable because of family responsibilities is also large (20.3%). Discouraged workers account for 5.8% of the total, whereas 7% are young people with an illness or (p.521) disability. Finally, 11.7% are young people who are inactive without having indicated the reason, and 6.4% are re-entrants. Looking at the total population of young people in Europe in 2013, 4% of those aged 15–29 years were short-term unemployed, whereas 3.6% were long-term unemployed and approximately 3.1% were outside the labor market and education because of family responsibilities.

Origins and future of the concept of NEETs in the European policy agenda

Figure 17.3 Composition of the NEET population aged 15–29 years at the EU level, percentage shares, 2013.

Source: Eurostat (EU-LFS 2013).

According to the proposed decomposition, we can say in broad terms that at the European level, the share of young people who are NEET for labor-market driven reasons amounts to 60.8% of the total, which corresponds to the sum of re-entrants, short- and long-term unemployed, and discouraged workers. Of these, half are at risk of long-term disengagement (being both long-term unemployed and discouraged workers) and will require more ad hoc reactivation measures in order to be reintegrated into the labor market.

The need for targeted measures becomes even more evident when the distribution of the composition of the NEET population is examined by gender. The gender composition of the various categories reveals that whereas young men dominated the categories of labor market-driven NEETs, more than 92% of NEETs attributing this status to family responsibilities are women (Figure 17.4). Although it is unfortunately not possible to determine how many in this category are voluntarily in this situation, the clear gender imbalance in the category suggests room for maneuver for policy interventions, including the promotion of support to young women through childcare and other social care for family members so as to foster their reintegration into the labor market or education. (p.522)

Origins and future of the concept of NEETs in the European policy agenda

Figure 17.4 Gender composition of the NEET population aged 15–29 years, 2013.

Source: Eurostat (EU-LFS 2013)

17.6.2. Heterogeneity of NEETs in a heterogeneous Europe

The unemployed are the largest group of NEETs in most countries, although there are some significant differences with regard to the proportions in long- or short-term unemployment (Figure 17.5). (p.523)

Origins and future of the concept of NEETs in the European policy agenda

Figure 17.5 Composition of the NEET population at member-state level in 2013.

Source: Eurostat (EU-LFS 2013).

The short-term unemployed are the largest category among NEETs in Austria, Belgium, Denmark, Finland, France, Germany, Luxembourg, the Netherlands, Sweden, and the United Kingdom. This group ranges from 39% in Luxembourg to 28% in Belgium and Finland. All of these countries are also characterized by a NEET rate below the EU average, indicating that young people manage to enter the labor market more rapidly (see Berloffa et al., this volume; Flek, Hála, and Mysíková, this volume). It is interesting to highlight that in almost all these countries, the share of those who are NEET because of illness or disability is higher than the EU average and that the proportion of discouraged workers is also (marginally) higher than average.

Conversely, in Ireland and in some Mediterranean and Central European countries, such as Croatia, Greece, Italy, Portugal, Slovenia, and Spain, the largest group of NEETs is composed of the long-term unemployed. Some of this is a result of the economic crisis, but it also indicates deeper structural problems in youth transitions from school to work. The size of this cohort ranges from 48% in Greece to 26% in Italy, and in all these member states it is well above the EU average. In both Italy and Croatia, the percentage of young people who are discouraged workers is also well above the EU average.

The gender composition of the NEET group is strongly polarized, and in the United Kingdom, Ireland, and Italy, the percentage of NEETs with family responsibilities is well above the EU average. This suggests that in most of these countries, being NEET not only appears to be driven by structural barriers in accessing the labor market but also may be largely attributable to additional disadvantages and family responsibilities (Gökşen et al. 2016).

The NEET rate in Eastern European countries varies across countries—from 12% in the Czech Republic to 25% in Bulgaria. The largest proportion of the NEET population in Eastern member states is attributable to those with family responsibilities—a category composed almost entirely of women. Although the gender dimension and family responsibilities are common drivers, member states differ as to how labor market factors affect the composition of the NEET population. In the Czech Republic, Latvia, Lithuania, and Poland, the share of those closer to the labor market—re-entrants and the short-term unemployed—is higher than the EU average. Conversely, the share of long-term unemployed and discouraged workers is well above the EU average in Bulgaria, Hungary, Romania, and Slovakia.

Considerable efforts have been made by EU member states to reintegrate some groups of NEETs through the use of the Youth Guarantee, especially the short-term unemployed and re-entrants. In many cases, member states have included provisions that address young people who are NEET because of illness or disability. Despite these efforts, few measures currently focus on long-term youth unemployment and especially on young mothers and those young people who cannot participate in the labor market because of family responsibilities (p.524) (Eurofound 2015). A more general observation is that some member states have tended to target job-ready young people with Youth Guarantee interventions rather than those who are furthest from the labor market (Eurofound 2016).

17.7. Conclusions

The concept of NEET and the NEET indicator have attempted to go beyond traditional indicators for youth labor market participation so as to provide a better understanding of youth vulnerability on the labor market. Although from a statistical standpoint it is very easy to capture the NEET population, NEETs are by definition a heterogeneous category combining groups with very different experiences, characteristics, and needs, which include both vulnerable and nonvulnerable young people. Addressing the heterogeneity of the NEET population is of key importance in order to make successful and optimal use of the NEET indicator for policymaking.

Although the overall NEET indicator does not allow us to understand the characteristics of this diverse population, this chapter disentangles the heterogeneity of the NEET population by proposing a disaggregation of the main indicator in seven types, each of which identifies a particular subgroup of young people with its own needs. If applied to the EU-LFS, the categorization could be used every year to monitor trends in the composition of the NEET population and the effectiveness of specific targeted policy interventions.

On the one hand, policy is rightly aimed at reducing overall NEET rates because these are clear indicators of the difficulties young people find in making the transition to work. On the other hand, addressing the heterogeneity of the NEET population has important consequences for appropriate policy responses for different groups of young people.

In particular, when used carefully and disaggregated in the manner outlined in this chapter, the NEET indicator can illustrate the particular needs of specific young people, such as young mothers and those with disabilities. This is preferred to a more traditional categorization implied by the label “inactive.” In order to effectively reintegrate NEETs, the different needs and characteristics of the various subgroups have to be taken into account because there will be no one-size-fits-all policy solution. Only a tailored approach for different subgroups has the potential to effectively and successfully reintegrate NEETs into the labor market and education.

The key groups who are still overlooked are those in the gray areas of education, training, and employment. Those who are in temporary or insecure forms of work and those who are underemployed, for example, are frequently in vulnerable and marginalized positions. Similarly, there are young people in education and training who can be regarded as reluctant conscripts: They have been “forced” to engage under threat of benefit (p.525) withdrawal or have been discouraged from entering the labor market by a perceived lack of opportunities. In this context, although new concepts will be difficult to operationalize, future analysis to map the landscape of youth opportunities needs to pick up both objective and subjective dimensions of vulnerability that characterize modern youth transitions so as to understand how effective policy implementation can address these different dimensions of disadvantage.

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Notes:

(1) The ILO definition of unemployment covers all people who are without work or were not in paid employment during the previous 4 weeks, who have actively sought work during the previous 4 weeks, and who are available to start work within the next fortnight (International Labour Organization 1982).

(2) (1) Seeking employment during the previous 4 weeks (SEEKWORK); (2) reasons for not looking for a job (SEEKREAS); (3) availability to start job within 2 weeks (AVAIBLE); (4) reasons for not being available to start a job (AVAIREAS); and (5) duration of unemployment (SEEKDUR).

(3) (SEEKWORK = 1–2) or (SEEKWORK = 3 and SEEKREAS = 1,5); or (SEEKWORK = 4 and AVAIBLE = 2 and AVAIREAS = 1).

(4) (SEEKWORK = 4 and AVAIBLE = 1 and SEEKDUR = 0–4).

(5) (SEEKWORK = 4 and AVAIBLE = 1 and SEEKDUR = 6–8).

(6) (SEEKWORK = 3 and SEEKREAS = 2) and (SEEKWORK = 4 and AVAIBLE = 2 and AVAIREAS = 5).

(7) (SEEKWORK = 3 and SEEKREAS = 3,4) and (SEEKWORK = 4 and AVAIBLE = 2 and AVAIREAS = 4).

(8) (SEEKWORK = 4 and SEEKREAS = 7).

(9) (SEEKWORK = 3 and SEEKREAS = 6,8,–1) and (SEEKWORK = 4 and AVAIBLE = 2 and AVAIREAS = –1,6,2).

(10) The most recent available data at the time of writing.