Using Survey Data for Spatial Analysis 2
|Convenor||Professor Nina Baur (Technische Universität Berlin )|
|Coordinator 1||Ms Linda Hering (Technische Universität Berlin)|
|Coordinator 2||Ms Cornelia Thierbach (Technische Universität Berlin)|
Xenophobic attitudes are widespread among the East German population. It is, however, not clear to what extent, this is attributable to socio-demographic and socio-economic factors on an aggregate level on the East German context as such, only to mention such phenomena like ageing of the East German population, East-West migration of well-trained and highly qualified people, high unemployment rates or lower levels of contact opportunities with regard to immigrants. The contribution to the conference will address this research gap by applying advanced approaches of context or multilevel analysis to the German General Social Survey (GGSS/ALLBUS).
In our presentation we focus on the significance of spatial externalities for individual labor market outcomes. In particular, we pose the question how the wider spatial context has to be operationalized in order to capture its significance for labor market outcomes. We test for different ‘zones of influence’ by comparing multilevel event history models including different spatially weighted variables.
Why do migrants prefer co-ethnic physicians to physicians of other ethnicities? This paper discusses mechanisms that intensify co-ethnic preferences for medical support while simultaneously accounting for the local opportunity structure of medical supply. For this purpose, two geocoded datasets were combined using GPS information. The two datasets include a survey of Turkish migrants conducted in Mannheim, Germany and a registry of physicians, identifying Turkish physicians through onomastic procedures. Central findings reveal the importance of controlling for local opportunity structures, which significantly influence the individual choice to visit a co-ethnic physician.
A school’s ‘normative climate’, which can be assumed to affect school achievement, is usually measured by averages (e.g. the mean agreement to relevant items). This practice can be questioned: An average value may be inappropriate for measuring dominant attitudes, and a normative climate may (also) be defined by heterogeneity and/or extreme attitudes among students. Consequently, a normative climate would (additionally) have to be measured by distributions, minimal/maximal values etc. We analyze German NEPS data, comparing multilevel structural equation models to reveal the dimensionality and measurement of ‘school climates’.