Socio-Economic Effects of Secondary School Tracking: Binding Teacher Recommendations vs. Free Parental Choice

这个项目旨在分析在德国，决定孩子被允许或必须上的中学轨道的分配规则的影响。更准确地说，有约束力的教师建议和自由的家长选择之间的差异将在教育成果的基础上进行评估。这些结果应该为如何最有效地分配学生提供信息，同时减少社会不平等。为此目的，首先审查制度环境的改变是否会导致转换率的因果性和重大转变，例如，如果取消具有约束力的教师推荐，转换率是否会转向高等学校形式。其次，它被问到这些孩子在中学开始时是否表现出色。为此，该项目详细阐述了对五年级学生重复率的因果影响。最后，这些结果将分别对有和没有移民背景的学生进行分析。为了确定利益的因果关系，将利用最近的政策改革，导致取消强制性教师推荐，使用未改革的州作为对照组。特别是，将进行综合控制方法（SCM）（见Abadie等人，2010）：使用数据驱动算法构建合成反事实，作为几个潜在控制单元的最佳加权平均值。然而，在这个项目中，开发并应用了SCM方法的分解（从州到地区级别）版本，它应该通过创建效果分布来提高估计和推断的精度。

This project aims to analyzing the influence of the assignment rule that determines the secondary school track in Germany, which the child is allowed to, or must attend. More precisely, differences between binding teacher recommendations and free parental choice are to be evaluated w.r.t. educational outcomes. These outcomes are supposed to provide information on how to allocate pupils most efficiently and, at the same time, decrease social inequality.For this purpose, it is first examined whether a change in the institutional setting leads to a causal and significant shift in transition rates, e.g., towards higher school forms if binding teacher recommendations are abolished. Secondly, it is asked whether these children perform successfully at the beginning of secondary school. For this purpose, the project elaborates on the causal effect on repetition rates for pupils in 5th grade. Lastly, such outcomes are to be analyzed separately for pupils with and without migration background. To identify the causal effects of interest, recent policy reforms are to be exploited that led to the abolition of mandatory teacher recommendations, using non-reformed states as the control group. Particularly, Synthetic Control Methods (SCM) will be conducted (see Abadie et al. 2010): a data-driven algorithm is used to construct a synthetic counterfactual as an optimally weighted average of several potential control units. In this project, however, a dis-aggregated (from state to district level) version of the SCM approach is developed and applied, which is supposed to increase precision in estimation and inference by creating effect distributions.