Search

The knowledge economy provides huge opportunities for economic growth and to become the cornerstone of future economic development by turning data into wisdom or human capital. Education, one aspect of the knowledge economy, exhibits a history divided into three stages: the apprenticeship era, the universal schooling era and the (future) life-long learning era. The spread of knowledge has accelerated owing to the different stages of knowledge production, in particular the printing press and now the internet

This technical brief analyses the relationship between immigrant status and educational expectations in PISA. Migration flows from outside and within the EU have increased in recent years, and this has raised the attention of policy makers and the general public, with special interests on the implications that those flows can have on, among other, the education system and the labour markets. At the same time, the EU has set the Europe 2020 headline target of reducing the share of early school leavers to 10 % within the EU. Early school leavers become generally disadvantaged socially and economically in later stages in life, so that it is important to better understand the motivations for leaving school and provide adequate policy solutions. The European Commission (2016, p. 3) indicates that early school leavers are more likely to come from immigrant student groups, as their “early school leaving rates are nearly twice as high as for the native population”. Yet it also emphasises that there is still a lack of evidence pointing to the underlying reasons. In particular, it is not clear whether, among early school leavers, immigrants students are more frequent due to specific reasons related to the status of immigrants or whether they are more frequent because immigrant students are more likely to possess the set of characteristics that are normally associated to early school leaving behaviour (such as belonging to low socio economic status). This study analyses the factors that are most strongly related to the probability to leave school early, putting special attention to immigrant status (by differentiating among first and second generation immigrants and, where possible, among EU and non-EU immigrants). To this end, we use OECD’s PISA data, which are the most widely employed data on international student assessment. Since early school leavers cannot directly be considered with these data, we focus on educational expectations, including the expectation to dropout early from school. As the related literature emphasises, these expectations are very closely linked to actually realised educational career patterns. Therefore, we can use expectations to gain insights on the factors influencing early school leaving. In addition, we also employ data from Eurostat to complement the picture on early school leavers and immigrants. First, we provide a range of descriptive data on immigrants and expected early school leavers. Second, we run a number of two-level logit regression models, including a range of student- and school-level variables. In particular, we consider all (available) EU Member States together, before providing results for each MS individually. Finally, we also distinguish more specifically between EU and non-EU immigrants in our regression models. The results show that, when controlling for individual and school characteristics, immigrant students do not structurally differ in their expected early dropout probability from natives across Europe. In other words, the reasons why students expect to leave school early are the same for both immigrant students and natives. This finding implies that it is more important to focus on the common factors that are associated with expected early school leaving. In particular, our results suggest that these are, at the students’ level, the socio-economic background of students, their epistemological beliefs and grade repetition, while, at the school level, the most consistent factor is the school’s mean expected early school leavers rate. The school-environment thus appears to play a key role in shaping educational expectations. Among the student-related factors, grade repetition is the most amenable by policy, so that grade repetition practices may be reconsidered by national policy makers., JRC.B.4-Human Capital and Employment

This technical brief analyses the regional distribution of skills in Italy and Spain. Educational attainment rates have frequently been used as an indicator of regional educational development in EU Member States (MS). These rates indicate significant regional disparities in education within countries. However, recent evidence shows that the quality of education, as measured by the level of specific skills, is more important than the number of years one spends in school, in particular when considering the relationship between the cognitive (and non-cognitive) skills and economic growth. International large scale assessments (ILSA) of student performance measure these cognitive skills in key areas. OECD’s Program for International Student Assessment (PISA) provides a very useful and important source of information of students' performance in key cognitive skills. When analysing PISA data, researchers and commentators often focus on cross-country comparisons. However, vast within-country differences exist, also in terms of educational attainment and PISA test scores. A focus on country averages alone would hence provide only a partial view of the status of education within countries. However, the possibility of exploring within-country differences with PISA data is limited to only a few countries. In this report we focus on regional inequalities in cognitive skills (as measured by PISA test scores) in Italy and Spain, using regional PISA data from the most recent 2015 wave, and we analyse the factors that are associated with these inequalities. In order to insure full comparability between the two countries we define regions at the level of NUTS1 (macro-region), following Eurostat’s official NUTS 2013 classification. We investigate regional inequalities by using descriptive statistics, by running a range of OLS regression models that allow us to analyse the associations between PISA 2015 science scores and the explanatory variables within regions and finally by using the Blinder-Oaxaca decomposition method to specify the factors that are related to within-country differences. The results show that there are significant regional differences in PISA scores within both MS. There are several factors that are associated with regional differences within Italy and Spain. The factors most consistently positively associated with regional science achievement are teacher-directed teaching and epistemological beliefs, while grade repetition and truancy are significantly negatively related with achievement. Still, there is also a range of other relevant factors varying between and within both MS. The Blinder-Oaxaca decomposition also shows that variables such as the socio-economic background, the students’ expected occupation, learning outside school time, truancy, immigrant status and grade repetition matter for within-country differences. Our results suggest that policy makers should focus on finding solutions to limit truancy and rethink grade repetition to leverage scores in lower performing regions. Moreover, our results with regard to epistemological beliefs and teaching practices challenge thinking about how science should be taught in schools in Italy and Spain. The specific results for each region may allow policy makers to consider more in detail how a region stands in comparison to the rest of the country, and the specific factors that need to be addressed to improve the within-country inequality related to educational achievement., JRC.B.4-Human Capital and Employment

The current economic crisis has put ever more to the forefront the need to achieve educational goals in the most efficient way. Therefore, this report provides an empirical analysis of the efficiency in education in the EU. Efficiency is measured first by using two different but related traditional frontier approaches (Data Envelopment Analysis and Free Disposal Hull) and then the robustness of our findings is checked by means of multi-criteria evaluation. The analysis is based on a number of standard variables from the literature. The results show, among others, that not the amount, but the specific use of resources is what matters; and that the efficiency of an educational system could also contribute to long-term benefits in terms of adults’ skills and competences., JRC.B.4-Human Capital and Employment