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Examining the Structure of Spatial Health Effects in Germany Using Hierarchical Bayes Models

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  • Peter Eibich
  • Nicolas R. Ziebarth

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.

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File URL: http://www.diw.de/documents/publikationen/73/diw_01.c.436062.de/diw_sp0620.pdf
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Bibliographic Info

Paper provided by DIW Berlin, The German Socio-Economic Panel (SOEP) in its series SOEPpapers on Multidisciplinary Panel Data Research with number 620.

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Length: 44 p.
Date of creation: 2013
Date of revision:
Handle: RePEc:diw:diwsop:diw_sp620

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Keywords: Spatial health effects; Hierarchical Bayes Models; Germany; SOEP; SF12;

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