Suchergebnisse

This chapter investigates how individual characteristics can explain school-to-work transitions that are associated with NEET status after leaving secondary school in the Netherlands. The Netherlands is a particularly interesting case to study youth who are Not in Employment, Education, or Training. In 2016, the Netherlands had the lowest NEET rate in the European Union. Many Dutch institutions and policies were a deliberate attempt to counter rapidly rising youth unemployment in the 1980s, when very high rates of youth unemployment, especially among the less educated, paired with and low outflow and educational crowding out were of great concern. Track placement in secondary education is determined by the pupils' score on a series of standardised performance tests on a number of indicators and a teacher evaluation, right at the end of elementary education.

Using new direct measures of numeracy and literacy skills among 85,875 adults in 17 Western
countries, we find that foreign-born adults have lower mean skills than native-born adults of the
same age (16 to 64) in all of the examined countries. The gaps are small, and vary substantially between countries. Multilevel models reveal that immigrant populations' demographic and socioeconomic characteristics, employment, and language proficiency explain about half of the cross-national variance of numeracy and literacy skills gaps. Differences in origin countries' average education level also account for variation in the size of the immigrant-native skills gap. The more protective labor markets in immigrant-receiving countries are, the less well immigrants are skilled in numeracy and literacy compared to natives. For those who migrate before their teens (the 1.5 generation), access to an education system that accommodates migrants' special needs is crucial. The 1 and 1.5 generation have smaller numeracy and literacy skills gaps in more ethnically diverse societies.

This article explores the extent to which macro-level characteristics of destination countries, origin countries, and immigrant communities help explain differences in immigrant children's educational achievement. Using data from the 2003 PISA survey, we analyze the mathematical performance of 7,403 pupils from 35 different origin countries in 13 Western countries of destination. While compositional differences offer some explanatory power, they cannot fully explain cross-national and cross-group variance. Contextual attributes of host countries, origin countries, and communities are also meaningful. In this regard, strict immigration laws explain immigrant children's better educational performance in traditional immigrant-receiving countries. Results further suggest that origin countries' level of economic development can negatively affect immigrant children's educational performance, and that immigrant children from more politically stable countries perform better at school. Finally, socioeconomic differences between immigrant communities and a native population, and relative community size, both shape immigrant children's scholastic achievement.

ICS Radboud University Nijmegen This article explores the extent to which macro-level characteristics of destination countries, origin countries, and immigrant communities help explain differences in immigrant children's educational achievement. Using data from the 2003 PISA survey, we analyze the mathematical performance of 7,403 pupils from 35 different origin countries in 13 Western countries of destination. While compositional differences offer some explanatory power, they cannot fully explain cross-national and cross-group variance. Contextual attributes of host countries, origin countries, and communities are also meaningful. In this regard, strict immigration laws explain immigrant children's better educational performance in traditional immigrant-receiving countries. Results further suggest that origin countries' level of economic development can negatively affect immigrant children's educational performance, and that immigrant children from more politically stable countries perform better at school. Finally, socioeconomic differences between immigrant communities and a native population, and relative community size, both shape immigrant children's scholastic achievement.

This chapter investigates how individual characteristics can explain school-to-work transitions that are associated with NEET status after leaving secondary school in the Netherlands. The Netherlands is a particularly interesting case to study youth who are Not in Employment, Education, or Training. In 2016, the Netherlands had the lowest NEET rate in the European Union. Many Dutch institutions and policies were a deliberate attempt to counter rapidly rising youth unemployment in the 1980s, when very high rates of youth unemployment, especially among the less educated, paired with and low outflow and educational crowding out were of great concern. Track placement in secondary education is determined by the pupils' score on a series of standardised performance tests on a number of indicators and a teacher evaluation, right at the end of elementary education.

This paper explores to what extent and how the risk that young mothers become NEET (Not in Employment, Education or Training) later in life is related to family policy provisions (i.e. parental leave and Early Childhood Education and Care). We examine a three-staged process: the relation between (a) characteristics of family policies and the use of it, (b) the use of family policy provisions and NEET risks, and (c) the effectiveness of family policy provisions on the characteristics of these family policies. Combining data from the EU-LFS with macro-level indicators of family policies, we analyse NEET risks of 13,613 young mothers (20-29) in 27 EU-countries. We find that young mothers are more likely to take parental leave if it is paid for a longer period of time, and are more likely to use ECEC when childcare placement is guaranteed. Both parental leave and ECEC services are associated with lower NEET risks, as long as they are not used for overly short or long periods. However, this depends largely on the way parental leave is organised. In addition, in countries where ECEC is more affordable, young mothers who use ECEC are better protected against NEET risks later in life.

This paper explores to what extent and how the risk that young mothers become NEET (Not in Employment, Education or Training) later in life is related to family policy provisions (i.e. parental leave and Early Childhood Education and Care). We examine a three-staged process: the relation between (a) characteristics of family policies and the use of it, (b) the use of family policy provisions and NEET risks, and (c) the effectiveness of family policy provisions on the characteristics of these family policies. Combining data from the EU-LFS with macro-level indicators of family policies, we analyse NEET risks of 13,613 young mothers (20-29) in 27 EU-countries. We find that young mothers are more likely to take parental leave if it is paid for a longer period of time, and are more likely to use ECEC when childcare placement is guaranteed. Both parental leave and ECEC services are associated with lower NEET risks, as long as they are not used for overly short or long periods. However, this depends largely on the way parental leave is organised. In addition, in countries where ECEC is more affordable, young mothers who use ECEC are better protected against NEET risks later in life.

In this article, we study the role of automation risks in the school-to-work transition (STWT) of secondary vocational education (VET) graduates in the Netherlands. We use a combination of administrative and survey data, enriched with estimates of automation risks. Using sequence analysis, we find four ideal-type school-to-work trajectories representing the first 10 years in the labor market: employment, further education, further VET, and NEET. Multinomial regressions show that automation risk is not consistently associated with the type of school-to-work trajectory. We also investigate the relation between automation risk and starting wages and wage growth. We find that automation risk is negatively associated with starting wages, but not with wage growth.

This paper investigates to what extent the likelihood of young people being long-term NEET can be explained by low literacy skills, how this varies across advanced countries, and how this cross-national variation can be explained by education and social policies. We use PIAAC data and include macro-level indicators on education and social policies. We analyze the likelihood of being long-term NEET versus being in employment or in education/training among some 34,000 young people aged 20-30 from 25 countries. We find that low-literate young people are more likely to be long-term NEET. While NEET risks are associated with countries' institutional characteristics, this does not mean that these characteristics and policies always work in favour of low-literate young people. Although high levels of (enabling) ALMP generally reduce the risk of being NEET, they do so less for low-literate young people. Additionally, young people living in social-democratic welfare states are less likely to be NEET, but low-literate young people seem to profit less from this.