Bordering the Area of Spatial Relevance for Schools: A stochastic Network Approach using the Example of Hamburg, Germany

Most approaches to spatial definitions of schooling markets are based on assumptions which may cause incorrect estimates. This paper presents stochastic network analysis as an alternative approach. Based upon individual student data of the metropolis Hamburg, the results are compared to those of traditional approaches. First, this article gives a short introduction to the current setting of Germany's educational system, the national efforts for school improvement and the relevance of an adequate spatial definition of a market in this context. Subsequently, the applied method and the essential data structure are described. Following the identification of local and regional schooling markets in Hamburg by applying stochastic network analysis, the authors quantify meso- and small-scale competition amongst schools involved in the transition of students from primary to secondary schools in context of social composition.