Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
7383935 | Regional Science and Urban Economics | 2014 | 16 Pages |
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 20Â 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.
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Authors
Peter Eibich, Nicolas R. Ziebarth,