Examining the structure of spatial health effects in Germany using Hierarchical Bayes Models

Peter Eibich and Nicolas Ziebarth ()

Regional Science and Urban Economics, 2014, vol. 49, issue C, 305-320

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 20years 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.

Keywords: Spatial health effects; Hierarchical Bayes Models; Germany; SOEP; SF12 (search for similar items in EconPapers)
JEL-codes: C11 C21 I12 I14 I18 (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (17)

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Related works:
Journal Article: Examining the Structure of Spatial Health Effects in Germany Using Hierarchical Bayes Models (2014)
Working Paper: Examining the Structure of Spatial Health Effects in Germany Using Hierarchical Bayes Models (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:regeco:v:49:y:2014:i:c:p:305-320

DOI: 10.1016/j.regsciurbeco.2014.06.005

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