Educational systems can typically be characterized according to two fundamental features: stratification and standardization. While “standardization” refers to the extent to which nationwide criteria for educational standards exist, “stratification” describes the extent to which the educational system is hierarchically stratified (i.e., the degree to which educational systems are tracked).
The German educational system is a classic example of high standardization coupled with high levels of stratification whereas the educational system of the United States represents the prototypical case of low standardization and low stratification. It has been argued that educational systems with a stronger focus on stratification or tracking manage to more effectively educate pupils, however at the cost of exacerbating social inequality in children’s educational achievement.
Based on cross-national comparative data, the focus of this project is to reinvestigate the “equality-efficiency-trade-off” with respect to ethnic educational inequality and potential long-term effects of educational systems on labor market outcomes using a variance function regression framework. This approach is ideally suited to tackle research questions involving hypotheses about distributional differences (i.e., means and variances combined) of two (or more) groups.
Funded by the German research foundation, starting in 2015. For more information, see here
Spörlein. Manuscript. Educational and skill mismatch of immigrants across European labour markets: devaluation and closure.
Spörlein. 2018. How educational systems structure ethnic inequality among young labour market participants in Europe: occupational placement and variation in the occupational status distribution. Research in Social Stratification and Mobility. DOI: 10.1016/j.rssm.2018.04.006.
Spörlein and Schlüter. 2018. How education systems shape cross-national ethnic inequality in math competence scores: Moving beyond mean differences. PLOS ONE 13: e0193738. DOI:/10.1371/journal.pone.0193738
Also: see https://github.com/chspoerlein/multi.varfun for my R-package to run mixed effects variance function regression.