Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/92498
Authors: 
Eibich, Peter
Ziebarth, Nicolas R.
Year of Publication: 
2013
Series/Report no.: 
SOEPpapers on Multidisciplinary Panel Data Research 620
Abstract: 
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.
Subjects: 
Spatial health effects
Hierarchical Bayes Models
Germany
SOEP
SF12
JEL: 
C21
C11
I12
I14
I18
Document Type: 
Working Paper

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.