【摘要】This paper uses Hierarchical Bayes Models to model and estimate spatial health effects in Germany. We combine rich individual-level household panel data from the German SOEP with administrative county-level data to estimate spatial county-level health dependencies. As dependent variable we use the generic, continuous, and quasi-objective SF12 health measure. We find strong and highly significant spatial dependencies and clusters. The strong and systematic county-level impact is equivalent to 0.35 standard deviations in health. Even 20 years after German reunification, we detect a clear spatial East–West health pattern that equals an age impact on health of up to 5 life years for a 40-year old.

【关键词】Spatial health effects; Hierarchical Bayes Models; Germany; SOEP; SF12

【文献来源】Peter Eibich; Nicolas R. Ziebarth.Regional Science and Urban Economics.2014（11）