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Volume 49, July/August 2014, Number 4 · pp. 221-227

Articles

How to Combat NEET? Evidence from Austria

Dennis Tamesberger, Heinz Leitgöb, Johann Bacher

The number young people not in education, employment or training (NEET) has been rising since the beginning of the economic and financial crisis in 2008. This article contributes to the discussion about the reduction of the NEET rate by addressing the questions: Which structural factors can explain differences in the NEET rates among Austrian federal states? What causes an increased or reduced NEET risk at the individual level? What are the key factors for an exit from a NEET situation?

Dennis Tamesberger, Chamber of Labour, Linz, Austria.

Heinz Leitgöb, Johannes Kepler University, Linz, Austria.

Johann Bacher, Johannes Kepler University, Linz, Austria.

Young people constitute one of the groups most deeply affected by the recent financial and economic crisis. Within the 28 member states of the EU, the number of unemployed youth (individuals under 25 years) has increased from 4.3 million in 2008 to 5.6 million in 2013.1 The narrow definition of unemployment in the ILO labour force concept only partly captures the extent of disengagement of young people.2 As a consequence, international labour market experts3 and the European Commission have recently used an additional indicator – the number of young people who are not in employment, education or training (NEETs) – to monitor the labour market and social situation of young people. The underlying assumption is that NEETs have a much higher risk of remaining unemployed, experiencing poverty, and being socially excluded in the future.4 According to the OECD,5 the concept of NEET undermines young people's ability to achieve self-sufficiency. Since the beginning of the financial and economic crisis, the NEET rate has increased from 10.8 per cent in 2008 to 13 per cent in 2013 in the 28 EU member states.6 This increase means that the number of NEETs has risen from 6.7 million to 7.7 million. This huge number of young people with a high risk of potentially permanent social exclusion highlights the urgent need for political measures and strategies to reduce the NEET rate significantly and sustainably – throughout Europe as well as at the national level. In July 2013, the European Council responded to this need by agreeing on a package to combat youth unemployment, including advancing efforts to fund youth guarantee schemes in the member states, and increasing youth mobility and involvement of the social partners. The European Council emphasised the importance of the sharing of national best practices.7

In response, the Intereconomics Forum on "Youth Unemployment" presented case studies and policy recommendations from England, Belgium, Spain, Poland, Ireland, and Germany.8 Maguire9 argues for a broad policy package including preventive and reintegration strategies, as well as additional active labour market policies, both of which stimulate demand for young employees. Recent events in Spain have revealed the need to reduce labour market dualism, which puts many young people on a precarious employment track.10 The empirical results from analysis of Poland indicate that training programmes increase the young person's chance of finding employment, especially for those who are not seeking employment at the moment.11 To create immediate employment possibilities for young people, Eichhorst, Hinte, and Rinne suggest temporary wage subsidies to employers.12 Less knowledge is available from those countries with the lowest NEET rates in the EU in 2013 – Luxembourg (5.0 per cent), the Netherlands (5.1 per cent), and Austria (7.1 per cent).13 It is an appropriate assumption that these countries have implemented measures to protect young people against unemployment and NEET circumstances, even during this and other periods of economic turmoil. This article aims to close this gap by analysing the effectiveness of measures and strategies to reduce the NEET rate in Austria, considered one of the "successful countries".14 Therefore, the following questions will be discussed:

  • Which structural factors can explain the differences between the NEET rates in the Austrian federal states?
  • What are the causes of increased/reduced NEET risk at the individual level?
  • Which are the decisive factors for an exit from the NEET situation?

In order to adequately answer the research questions, we conducted analyses within three scopes. First, we analysed structural characteristics of the federal states that seem likely to influence the NEET rate. Second, we explored determinants or risk factors that contribute to a high NEET rate. Reducing these risk factors would be a preventive strategy. Third, we identified factors for a reintegration strategy that would successfully help to overcome a NEET situation.

Methodology

The underlying data of the quantitative analyses are based on the 2006-2011 data of Austria's quarterly micro-census.15 These data were merged into a panel dataset (the design of the micro-census is based on a five-panel rotation scheme), which includes a total of 25,332 youths aged between 16 and 24 and which is representative of Austria. Because several persons from the previously defined target population might live in one selected household, and because disproportional sampling was used for the federal states, a design effect (deff = 1.50; neff = 16,922) must be taken into account.16 We decided to implement an adjustment factor into the household weight to obtain a sample size equal to neff . Apart from descriptive procedures, we applied OLS-based linear regression models for data analysis.17

Results
Measures at the federal state level

The "NEET situation" refers to circumstances in which young people are not integrated in three important social subsystems: the employment system, the educational system, and the training system. The last system is mainly realised by an active labour market policy. All nine of Austria's federal states have different policies and financial resources that shape these three subsystems; we can assume that some federal states are better at integrating young people than others. According to our analysis, among 16-24-year-olds, Vienna has the highest NEET rate (12.2 per cent) and Upper Austria the lowest (7.0 per cent) on average between 2006 and 2011. After controlling for social structural differences concerning gender, migration background, age, and population size, the significant differences between the federal states' NEET rates disappear almost completely, meaning that the relatively high NEET rate in Vienna can be mainly explained by the less favourable social structure of a large city. Nevertheless, it cannot be immediately ruled out that structural characteristics of the federal states (schooling provision, number of vacancies and labour market policy) may also influence the NEET rate.

To evaluate this hypothesis, we have applied regressions at the federal state level (ecological regressions). The results show significant effects of the per capita expenditure on active labour market policy and the number of available jobs as an indicator for economic performance of the federal states. Schooling provision, measured by the number of teachers in upper secondary schools, has no significant influence on the NEET rate. After controlling for social structure, the effects are generally weak and only significant for the male population, meaning that the NEET risk of young women cannot be explained by the analysed structural characteristics of the federal states.

In accordance with these results, a reduction of the NEET rate among the male population can be achieved through the following structural measures at the federal state level:

  • Increasing the expenditures on active labour market policy per each unemployed youth, and/or improving the effectiveness of active labour market policy. The variable is significant, therefore both measures are adequate. Conversely, a reduction in the per capita expenditure on active labour market policy would lead to a higher NEET rate.
  • Increasing the number of available jobs in the working-age population and/or improving matches between available jobs and unemployed individuals. The fact that this variable is significant indicates that fiscal stimulus measures would reduce the NEET rate.
  • Changing the impact of upper secondary schools. Only when this variable has a significant negative effect on the NEET rate will an expansion of schooling provision reduce that rate.
Table 1 (back to the text)
Effectiveness of measures to reduce the NEET rate at the federal state level

Percentage point reduction in the NEET rate when changing the value of the variable or changing the impact of the variable by ...

Actual

Variable

value 10% 20% 30%
Per capita expenditure for active labour market policy per each unemployed youth in €1,000

7.77

-.240

-.480

-.721
Number of available jobs per 1,000 persons in the working age population

7.72

-.220

-.439

-.659
Number of teachers in upper secondary schools (secondary academic school, VET school, VET college, training schools for kindergarten teachers/educators) per 1,000 persons aged between 16 and 20 years

60.7

In order to achieve a reducing effect, the variable must influence the NEET rate negatively. This is not the case at the moment.

Reading aid: A 10 per cent increase in the per capita expenditure on active labour market policy for each unemployed youth from about €7,770 to €8,547 would reduce the given NEET rate by about 0.24 percentage points. An equal reduction can be achieved by increasing the measure's impact by 10 per cent.

Sources: Federal Ministry of Education and Women's Affairs; Austrian micro-census; Statistik Austria; Federal Ministry of Labour, Social Affairs and Consumer Protection; own calculations. For more details see J. Bacher, D. Tamesberger, H. Leitgöb: Unterstützung der arbeitsmarktpolitischen Zielgruppe "NEET", Teilbericht 1: Literaturüberblick & Quantitative Analyse, Linz 2013, ISW.

 

Table 1 shows the effectiveness of measures to reduce the total NEET rate at the federal state level. For example, a 20 per cent increase in the per capita expenditure on active labour market policy for each unemployed youth would reduce the NEET rate by about 0.48 percentage points. Similarly, increasing the number of available jobs by about 20 per cent would reduce the NEET rate by about 0.44 percentage points. Again, in Austria, these measures would only affect young men.

At this point, it becomes obvious that to significantly reduce the NEET rate (i.e. by at least one percentage point) through federal level measures, massive efforts are necessary. There are two main reasons for the weak effects. First, the NEET rate in Austria is already relatively low, and a further reduction would be difficult. Second, we analysed only structural measures at the state level, not measures at the individual level. The next section of this paper will address the effectiveness of measures taken at the individual level.

These results are consistent with current findings of cross-country analysis within the EU. Eurofound identified a context effect regarding expenditure on the active labour market policy.18 This effect is also significant after accounting for country fixed effects and changing labour market conditions. Further, Eurofound emphasised that NEET risks are strongly related to the tightness of the labour market.19 Thus, promoting programmes that create jobs is an effective strategy for combating NEET.

Preventive and risk factors at the individual level

As described above, political strategies or measures have a preventive effect if they are able to reduce individual risk factors relating to NEET. To identify these factors, we estimated linear regression models – separately for female and male youth, as it was assumed that influencing factors may apply differently to young women and men. In Austria, the NEET risk among young women is associated with early school leaving, childcare responsibilities for children under the age of three, health-related impairments, and preliminary unemployment experiences. Early school leaving has the strongest effect because it has an direct influence on the NEET risk, as well as an indirect effect via care responsibilities and unemployment experiences. The NEET risk factors for young men are early school leaving, health-related impairments, and preliminary unemployment experiences.

Based on these results, political strategies can be derived in two ways. First, measures can aim to change the value of a direct risk factor – for example, by lowering the share of early school leavers. Second, but less widely recognised, measures also can aim to reduce the correlation between a risk factor and the NEET rate. An example of this strategy would be offering more jobs specifically to early school leavers, to reduce the correlation between early school leaving and a high NEET risk. Based on this initial consideration, we can formulate the following strategies with the potential to reduce the NEET rate:

  • Reducing early school leaving among youth and/or reducing the relationship between early school leaving and NEET risk. In Austria, youth coaching is an example of this approach. This transition management was introduced by the Federal Ministry for Education, the Arts and Culture (BMUKK) in cooperation with the Federal Ministry of Labour, Social Affairs and Consumer Protection (BMASK), and implemented in the federal states by the Federal Social Office (BSB) in 2013.20 An alternative strategy would be expanding the supply of supra-company training, or providing incentives for companies21 to hire more apprentices with learning disabilities or poor grades in school. These measures would lead to a reduction of the NEET rate among both young women and young men.
  • Reducing illnesses or impairments and/or minimising the relationship between illnesses or impairments and NEET risk. An example of this approach would be improving rehabilitation opportunities or providing more job opportunities for young people with illnesses or impairments (i.e. a second labour market). These measures would lead again to a reduction in the NEET rate among both young women and young men.
  • Reducing unemployment experiences and/or reducing the relationship between unemployment experiences and NEET risk. One example of this approach is the Austrian training guarantee.22 Unemployment experiences cannot be avoided completely, but it is important to keep unemployment periods as short as possible in order to avoid negative consequences.23 These measures would lead to a reduction of the NEET rate among both young women and young men.
  • Reduction of the relationship between early pregnancy and NEET. This approach includes measures to prevent early involuntary pregnancies, as well as better childcare provision, which can help young women complete or start additional education or find employment, if desired. These measures would reduce the NEET rate among young women.
Table 2 (back to the text)
Effects of reducing individual risk factors on the NEET rate

Percentage point reduction in the NEET rate when changing the value of the variable or changing the impact of the variable by ...

Variable

Actual

value

10% 20% 30%
Young women
Early school leaving

9.2

-.297

-.594

-.891
Illnesses/impairments

1.1

-.042

-.084

-.126
Children under age 3

7.3

-.196

-.393

-.589
Unemployment
experiences

4.3

-.096

-.193

-.289
Young men
Early school leaving

9.5

-.199

-.397

-.596
Illnesses/impairments

1.0

-.036

-.073

-.109
Children under age 3

2.9

not significant

Unemployment
experiences

4.9

-.134

-.269

-.403

Reading aid: A 10 per cent decrease of the proportion of early school leavers from 9.2 per cent to 8.3 per cent would reduce the given NEET rate by about .297 percentage points. An equal reduction can be achieved by reducing the impact of early school leaving by 10 per cent.

Source: Austrian micro-census; own calculations.

 

Table 2 estimates the size of the effects of these measures on the NEET rate. We analysed three scenarios: a 10, 20, and 30 per cent reduction of the value of variables or of their impact. The strongest impact on NEET would be a reduction in early school leaving. Reducing the early school leaving rate by about 20 per cent (it is currently 9.2 per cent among young women and 9.5 per cent among young men between 16 and 24 years of age) would reduce the NEET rate by about 0.59 percentage points among young women and 0.40 percentage points among young men. The same effect can be achieved by reducing the relationship between early school leaving and the NEET risk, for example by providing more adequate employment and training opportunities for young people who leave school early. An appreciable reduction of the NEET rate among young women could be achieved by reducing their childcare responsibilities for children under three years of age, or by reducing the impact of those responsibilities. Unintended pregnancies aside, having children is a socially desirable and financially supported position. As such, the aim should be to reduce the impact of the variable by providing more public childcare options for children under three years of age.

These measures have a strong effect because they are preventive and avoid the occurrence of a high NEET rate. However, there is also a need for strategies and measures aimed at helping young people who are already in a NEET situation and need help to enter the labour market or get back into the education system.

Reintegration measures to overcome a NEET situation

In Austria, half of the NEET population (47 per cent) succeeds in overcoming NEET during the observation period of 1.25 years (15 months). An exit from NEET status means that a young person was in a NEET situation for up to three quarters (nine months) but is in employment, education, or training during the last two quarters (six months). Approximately 38 per cent are considered to have permanent NEET status (in a NEET situation for 1-1.25 years, or 12-15 months). Other states, like temporary exits from NEET status or oscillating between NEET and non-NEET status, are empirically seldom. According to our results, females more easily succeed in leaving NEET status if they actively look for work and live in urban areas.24 The influence of location of residence is possibly due to the fact that childcare facilities are more prevalent in cities in Austria. On the other hand, for the age group between 20 and 24 years, childcare responsibilities and early school leaving make an exit from NEET status more difficult. For male NEETs, early school leaving and illnesses make leaving NEET status more difficult. Unlike for female NEETs, having children and searching for a job does not impact the likelihood of leaving NEET status for male NEETs. One explanation for the latter may be that the labour market position of young men is more dependent on economic trends (see Table 2), because men are more frequently employed in industrial sectors. Hence, individual behaviours like job seeking are less effective at helping male NEETs overcome a NEET situation. From this, we can conclude that female NEETs aged between 20 and 24 years, female NEETs living in rural areas, male NEETs with illnesses or disabilities, and youth of both genders who depart school early should be particular target groups for measures.

Until this point, we have only analysed factors that influence a general exit from NEET status. Overcoming NEET status may consist of undergoing training, making a second attempt at completing one's education, or finding employment. In Austria, the majority of NEETs who succeed in overcoming NEET are employed (70.7 per cent). Regarding policy measures, it is therefore important to know and consider which factors influence the labour market reintegration of NEETs. Table 3 presents the results for male and female NEETs who are looking for work.25 In Austria, about half of the NEET population is actively seeking employment. The majority of them (85.7 per cent) have contacted the public employment service (PES) and are thereby directly reachable for labour market policy measures. The results show that female NEETs who participated in a training course more easily find a job in the following quarter. A similar effect is caused by previous employment periods. An aggravating factor for NEET female youths is early school leaving. In contrast, taking part in a course has no positive effect concerning the employment of young men. For male NEETs, the relevant factors are age, occurrence of previous employment, and the year 2007. Older male NEETs between 20 and 24 years of age can more easily secure employment than younger NEETs. One explanation could be that older male NEETs have already finished their military or civil service.

Table 3 (back to the text)
Employment in t3
Females Males Difference in coefficients
b Beta p b Beta p t p
constant

.364

-

.017

.076

-

.536

1.472

.135
JOB_t2

.436

.425

< .001

.494

.480

< .001

-.725

.307
TRAINING_t2

.197

.140

.017

.078

.045

.371

.990

.244
INTERVIEW

-.011

-.011

.865

.019

.019

.730

-.352

.375
INFORMAL

.010

.024

.707

.030

.067

.201

-.554

.342
SELF-EMPL

-.033

-.017

.761

.054

.028

.577

-.597

.334
JOBCENTER

.017

.034

.581

-.028

-.056

.292

1.109

.216
HANDI

.180

.070

.213

-.042

-.017

.736

1.164

.203
MIGRA

-.025

-.021

.766

-.023

-.021

.746

-.018

.399
AGE20+

-.020

-.020

.744

.135

.130

.017

-1.870

.069
URBAN

.002

.002

.971

-.013

-.013

.797

.198

.391
CRISIS

.039

.038

.542

.045

.043

.412

-.070

.398
Y2007

-.036

-.030

.629

.164

.129

.016

-1.992

.055
EARLY

-.199

-.200

.001

.009

.009

.865

-2.532

.016
CITIZ

-.069

-.050

.494

.015

.012

.855

-.647

.324
CHILD3Y

-.125

-.070

.227

-.094

-.038

.466

-.183

.392

.282

.275

Note: For all variables, 1 = yes. JOB_t2 – being employed in t2; TRAINING_t2 – active participation in a vocational training course in t2; INTERVIEW – job interview in t2; INFORMAL – searching for job offers on one's own initiative; SELF-EMPL – self-employment as alternative strategy for leaving NEET status; JOBCENTER – contact to the job centre; HANDI – (chronic) diseases and health-related impairments; MIGRA – immigration background (first generation); AGE20+ – aged 20-24; URBAN – living in an urban area (population: 31,000 or above); CRISIS – year of first participation in micro-census survey is 2009 or 2010; Y2007 – year of first participation in micro-census survey is 2007; EARLY – early school leaving; CITIZ – citizen of Austria or another EU25 country; CHILD3Y – presence of a child of up to three years of age in household.

Source: Austrian micro-census; own calculations.

 

Conclusion and recommendations

This article aimed to identify measures to significantly reduce the NEET rate and address the questions: Which structural factors can explain differences in the NEET rates among Austrian federal states? What causes an increased or reduced NEET risk? What are the decisive factors for a successful exit from a NEET situation? To address these questions, we estimated: (a) the impact of certain measures (e.g. expenditure for active labour market policy, number of available jobs, and number of teachers in upper secondary schools) on the NEET rate at the federal state level; (b) the impact of preventive measures that address NEET risk factors; and (c) the impact of reintegration measures based on factors to overcoming a NEET situation.

At the federal state level, active labour market policy is effective at reducing the NEET rate significantly. The NEET rate can be reduced by boosting spending on labour market policies for young people and/or by improving the effectiveness of existing active labour market policies. Fiscal stimulus measures are able to reduce the NEET rate by creating more jobs. At the European level, Eurofound came to a similar conclusion.26 If the plausible policy lesson from these findings is boosting economic demand and creating jobs through fiscal stimulus packages, then public policy efforts should focus on expanding access to public childcare facilities. Doing so would have several benefits for the NEET target group. Investment in childcare services would have a significant positive impact on employment rates and would pay for itself within only four years.27 It would also help young parents in a NEET situation to reconcile work- or education-related responsibilities and family-related priorities, thereby helping to overcome the NEET situation and unemployment. Generally, we agree with Simmons and Thompson that an extensive Keynesian-style programme of public work is necessary to overcome the shortage of sustainable jobs in the EU.28 In that context, Matsumoto et al. criticised heavily the fiscal austerity programmes that impair the fiscal room in which the EU member states can manoeuvre.29

At the individual level, preventive strategies should aim to reduce NEET risk factors. In Austria, the main risk factors for women are early school leaving, childcare responsibilities, preliminary unemployment experiences, and illnesses or impairments. For young men, the same factors apply except for childcare responsibilities. Preventing early school leaving has the greatest potential for reducing the NEET rate. Because early school leaving is a complex and multi-faceted social process, addressing it effectively requires the implementation of a comprehensive approach on different levels and across different fields.30 At the level of (supra-)national educational policy, measures aimed at reducing the dependence of educational success and school performance from ascriptive characteristics (e.g. social status of parents, immigration background, and gender) are the means of choice. These approaches include investments in early childhood and preschool education,31 the development of a comprehensive school system with a late age of first selection,32 index-based distribution of additional funds in order to financially support schools with a socially disadvantaged student population,33 and granting schools a level of autonomy concerning the use of financial resources and educational design.34

Preventing early school leaving at the individual level is the aim of youth coaching, which was established as transition management in 2013 throughout Austria. It is based on an early warning system that enables youth coaches to identify and engage young people at risk of social exclusion in their ninth grade year, to support them with guidance, consultation, or case management.35 A first evaluation36 uncovers positive effects concerning the career choice, motivation, and self-perception of the young people. The recommendation of the Council37 to introduce youth guarantee schemes throughout the EU addresses central NEET risk factors, namely unemployment experiences and early school leaving. If the youth guarantee schemes are properly introduced, they are very likely to reduce NEET rates in the EU member states. One precondition for this is a sufficient available budget. Within the Youth Employment Initiative, EU member states with a youth unemployment rate above 25 per cent will receive financial support. For this purpose, the EU Budget includes €3 billion and the European Social Fund includes another €3 billion.38 According to ILO calculations, the total cost for establishing youth guarantee schemes in the eurozone would be approximately €21 billion.39 Therefore, the provided funding for the EU member states seems far too small, and an increase is vital. It is perhaps obvious that countries with the most problematic youth labour markets do not have the financial resources to establish youth guarantee schemes by themselves.

With regards factors to overcome a NEET situation, the analysis showed that female NEETs who live in urban areas and who actively seek employment are more frequently leaving behind NEET status. Participation in a training course and previous employment periods each have a positive impact on the labour market integration of young female NEETs. Again, measures against early school leaving and expanding public childcare facilities seem to be central for the reintegration of young women. For male NEETs, barriers to overcoming a NEET situation are early school leaving and illnesses or impairments. Previous employment periods and an age between 20 and 24 years have positive impacts on the labour market integration of young men. Interestingly, the positive impact of training courses could not be verified for young men, demonstrating the need for further research on this topic. Lutz and Mahringer40 explain the effectiveness of courses for women based primarily on the fact that courses are developed specifically to address the needs of women, meaning that the effectiveness of courses for young men could be improved by responding better to the needs of young men or using more individualised and flexible teaching methods.

The empirical result that previous employment periods have a positive impact on the labour market integration of NEETs allows two conclusions: first, employment experiences or direct contact with firms should be facilitated. Through internships, young people have the chance to receive better orientation regarding working conditions in a firm or in a certain profession. Companies also gain the opportunities to interact face-to-face with the young person and observe their competences and motivations, which provide a more complete picture than school grades alone. A German study shows for example that low-achieving job applicants who left school early receive more vocational training opportunities when employers have first-hand interactions with them.41 Nevertheless, the need exists for emphasising that internships must feature fair wages, good working conditions,42 and include close supervision and guidance by experienced personnel – this last factor is especially important for the NEET group. Second, the retention rate in employment should be increased. In other words, it is important to help employed young people to keep their jobs. Follow-up care with a case management approach can provide valuable support for young employees at the beginning of their working lives. In that context, the strong spread of temporary or fixed-term forms of employment seem to be problematic because it puts especially disadvantaged young people on precarious and unsecure employment tracks.43

Finally, this article identifies gaps in research concerning the health situation of young people. Illnesses and impairments of young people were identified as NEET risk factors at the individual level. Formulating useful recommendations will require first deepening knowledge about the types of diseases most likely to contribute to a NEET situation, and therefore looking beyond the Austrian micro-census, which does not provide any information on this topic.

  • 1 Eurostat database.

  • 2 According to the ILO labour force concept, a person is only classified as unemployed if the individual had not worked for one or more hours per week, is available for work within the next two weeks and had taken proactive steps to find a job. All three preconditions have to be fulfilled to be classified as unemployed, see: http://www.ilo.org/public/english/employment/yen/whatwedo/projects/indicators/2.htm.

  • 3 Eurofound: Young people and NEETs in Europe: First Findings, Dublin 2011; Eurofound: NEETs, Young People not in Employment, Education or Training: Characteristics, Costs and Policy Responses in Europe, Luxembourg 2012, Publications Office of the European Union.

  • 4 European Commission: On EU indicators in the field of youth, Commission staff working document, Brussels 2011.

  • 5 OECD: Youth Neither in Employment, Education nor Training (NEETs), in: Society at a Glance 2014: OECD Social Indicators, 2014, OECD Publishing.

  • 6 Eurostat database.

  • 7 European Council: Conclusions – 27/28 June 2013, Brussels 2013.

  • 8 See Intereconomics – Review of European Economic Policy: Youth Unemployment, Vol. 48, No. 4, 2013, pp. 196-235.

  • 9 S. Maguire: What Measures Can Be Taken to Address the Specific Problem of Young People Who Are NEET?, in: Intereconomics – Review of European Economic Policy, Vol. 48, No. 4, 2013, pp. 196-201.

  • 10 J.J. Dolado, F. Felgueroso, M. Jansen: Spanish Youth Unemployment: Déjà Vu, in: ibid., pp. 201-215.

  • 11 I. Styczyńska: Enhancing Youth Opportunities in Employment: Determinants and Policy Implications, in: ibid., pp. 215-223.

  • 12 W. Eichhorst, H. Hinte, U. Rinne: Youth Unemployment in Europe: What to Do About It?, in: ibid., pp. 230-235.

  • 13 Eurostat database. Our calculation with a NEET rate of 7.6 per cent in 2012 differs from the Eurostat data (6.5 in 2012). The difference can be mainly explained by the circumstance that Eurostat also includes young people aged 15 years. We use the 16-24 age group because compulsory education in Austria ends after nine years of school, which tends to be at the ages of 15 and 16. This means that if we analysed youth aged 15-24 in Austria, then the NEET rate would be lower because of compulsory education, and we would therefore underestimate the transition problems faced by youth in Austria. A further difference lies in whether for the Eurostat calculation an average of all survey waves is used or only the first one. We used only the first one because in following surveys NEETs are less likely to be at their original address. Hence, if an average of all survey waves were used, it would lead to an underestimation of the NEET rate. For more details, see J. Bacher, D. Tamesberger, H. Leitgöb: Unterstützung der arbeitsmarktpolitischen Zielgruppe "NEET", Teilbericht 1: Literaturüberblick & Quantitative Analyse, Linz 2013, ISW, p. 62.

  • 14 This article combines individual research results of the study to support the labour market policy target group "NEET", which the Institute of Social and Economic Sciences (ISW) has carried out in cooperation with the Johannes Kepler University of Linz (Institute of Sociology), the Research Institute for Vocational Training and Adult Education (IBE), the University of Vienna (Institute of Sociology), and okay.zusammenleben Vorarlberg on behalf of the Federal Ministry of Labour, Social Affairs and Consumer Protection. The complete study is available at: http://www.isw-linz.org/index.php/projekt-qneetq-jugendliche-sp-632316506.

  • 15 For details see J. Kytir, B. Stadler: Die kontinuierliche Arbeitskräfteerhebung im Rahmen des neuen Mikrozensus, in: Statistische Nachrichten, No. 6/2004, 2004, pp. 511-518.

  • 16 For details see, for example, E.S. Lee, R.N. Forthofer: Analyzing Compley Survey Data, Thousand Oaks 2006, Sage.

  • 17 See J. Bacher, D. Tamesberger, H. Leitgöb, op. cit.

  • 18 Eurofound: NEETs, Young People ..., op. cit.

  • 19 Ibid.

  • 20 Federal Ministry of Labour, Social Affairs and Consumer Protection: Youth and Work in Austria, Reporting Year 2012/2013, Vienna 2013.

  • 21 In Austria, firms get high financial incentives if they hire apprentices. In 2012, about €159 million was spent on this kind of subsidy, but the incentives are heavily criticised for a large deadweight loss effect. See for example K. Wacker: Teure neue Lehrstelle, Eine Untersuchung zur Effizienz des Blum-Bonus, Wien 2007, AK NÖ.

  • 22 Federal Ministry of Labour, Social Affairs and Consumer Protection: Aktive Arbeitsmarktpolitik in Österreich 1994-2013, Vienna 2013.

  • 23 On the influence of youth unemployment on future unemployment risks and on income development see, for example, A. Schmillen, M. Umkehrer: The Scars of Youth – Effects of Early-Career Unemployment on Future Unemployment Experience, IAB-Discussion Paper No. 6/2013, Nuremberg 2013, IAB; N.O. Skans: Scarring Effects of the First Labour Market Experience: A Sibling Based Analysis, Working Paper 2004: 14, Uppsala 2004, IFAU; concerning the consequences on later health condition or the life satisfaction, see D.N.F. Bell, D.G. Blanchflower: Young People and the Great Recession, in: Oxford Review of Economic Policy, Vol. 27, No. 2, 2011, pp. 241-267.

  • 24 J. Bacher, D. Tamesberger, H. Leitgöb, op. cit.

  • 25 Information about contact with the PES or job seeking behaviour is only available for those looking for a job. Therefore, we are only analysing this group. In order to check the robustness of our linear estimates we additionally fitted logit models. We found no substantial differences between the estimates from these two classes of models. For the comparison of the regression coefficients between males and females we rely on a test proposed by C.C. Clogg, E. Petkova, A. Haritou: Statistical Methods for Comparing Regression Coefficients Between Models, in: The American Journal of Sociology, Vol. 100, No. 5, 1995, pp. 1261-1293.

  • 26 Eurofound: NEETs, Young People ..., op. cit.

  • 27 D. Ahrendt et al.: Caring for Children and Dependants: Effect on Careers of Young Workers, Background papers, Dublin 2013, Eurofound.

  • 28 R. Simmons, R. Thompson: NEET Young People and Training for Work. Learning on the Margins, Stoke on Trent 2011, Trenham Books.

  • 29 M. Matsumoto, M. Hengge, I. Islam: Tackling the Youth Employment Crisis: A Macroeconomic Perspective, Geneva 2012, ILO.

  • 30 European Commission Education and Training: Reducing Early School Leaving: Key Messages and Policy Support, 2013,

  • 31 J. Bacher, H. Leitgöb: Testleistungen und Chancengleichheit im internationalen Vergleich, in: C. Schreiner, U. Schwantner (eds.): PISA 2006, Österreichischer Expertenbericht zum Naturwissenschafts-Schwerpunkt, Graz 2009, Leykam, pp. 195-205; J.J. Heckman: Schools, Skills, and Synapses, in: Economic Inquiry, Vol. 46, No. 3, 2008, pp. 289-324; OECD: Learning for Tomorrow's World, First Results from PISA 2003, Paris 2004, OECD.

  • 32 For details see J. Bacher, H. Leitgöb, op. cit.; E.A. Hanushek, L. Woessmann: Does Educational Tracking affect Performance and Inequality? Differences-in-differences Evidence across Countries, CESifo Working Papers No. 1415, 2005; D. Horn: Age of Selection Counts: A Cross-country Comparison of Educational Institutions, MZES Working Paper No. 107, 2008; G. Schütz, H.W. Ursprung, L. Woessmann: Education Policy and Equality of Opportunity, IZA Discussion Paper No. 1906, 2005; H.G. Van de Werfhorst, J.J.B. Mijs: Achievement Inequality and the Institutional Structure of Educational Systems: A Comparative Perspective, in: Annual Review of Sociology, Vol. 36, 2010, pp. 407-428.

  • 33 J. Bacher, H. Altrichter, G. Nagy: Ausgleich unterschiedlicher Rahmenbedingungen schulischer Arbeit durch eine indexbasierte Mittelverteilung, in: Erziehung & Unterricht, Vol. 160, pp. 384-400.

  • 34 OECD: PISA 2006. Science Competencies for Tomorrow's World, Vol. 1, Paris 2007, OECD.

  • 35 Bundessozialamt: Jugendcoaching: Umsetzungsregelungen inklusive Erläuterungskommentar und Beilagen, available at: http://www.nmsbruesslgasse.at/hs/pdf/Jugendcoaching_Umsetzungsregelungen.pdf.

  • 36 M. Steiner, G. Pressl, E. Wagner, J. Karaszek: Evaluierung Jugendcoaching, Vienna 2013, IHS.

  • 37 Official Journal of the European Union, 22 April 2013, C 120/1.

  • 38 European Commission: EU Measures to Tackle Youth Unemployment, MEMO/13/968, 8 November 2013.

  • 39 European Commission: Youth Employment: Commission Proposes Package of Measures – Frequently Asked Questions, MEMO/12/938 05/12/2012, Brussels 2012.

  • 40 H. Lutz, H. Mahringer: Wirkt die Arbeitsmarktförderung in Österreich? Überblick über Ergebnisse einer Evaluierung der Instrumente der Arbeitsmarktförderung in Österreich, in: WIFO Monatsberichte, Vol. 80, No. 3, 2007, pp. 199-218.

  • 41 H. Solga, B. Kohlrausch: How Low-achieving German Youth Beat the Odds and Gain Access to Vocational Training – Insights from Within-Group Variation, in: European Sociological Review, Vol. 29, No. 5, 2013, pp. 1068-1082.

  • 42 For more on this topic see European Quality Charter on Internships and Apprenticeship, http://qualityinternships.eu/.

  • 43 A. Furlong: The Zone of Precarity and Discourses of Vulnerability: NEET in the UK (Comparative Studies on NEET, Freeter, and Unemployed Youth in Japan and the UK), in: The Journal of Social Sciences and Humanities, Vol. 381, 2013, pp. 101-121.


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