Examining the Structure of Spatial Health Effects in Germany Using Hierarchical Bayes Models - Peter Eibich; Nicolas Ziebarth (Regional Science and Urban Economics, 2014)
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.
Additional information: NOTICE: This is the author's version of a work that was accepted for publication in "Regional Sciences & Urban Economics". Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Regional Science & Urban Economics 49 (2014), pp.305-320 and is online available at http://dx.doi.org/10.1016/j.regsciurbeco.2014.06.005.
Keywords: C21; C11; I12; I14; I18; 330; Spatial health effects; Hierarchical Bayes Models; Germany; SOEP; SF12; Gesundheitspolitik; Gesundheitswesen; Statistische Methode; Panel