Vortrag
Examining the Structure of Spatial Health Effects Using Hierarchical Bayes Models

Peter Eibich, Nicolas R. Ziebarth


First Bayesian Young Statisticians Meeting (BAYSM 2013)
Mailand, Italien, 05.06.2013 - 06.06.2013



Abstract:
This paper makes use of Hierarchical Bayes Models to model and estimate spatial health effects. We focus on Germany, combining rich individual-level household panel data with administrative county¿level information to estimate spatial county-level health dependencies. As dependent variable, we use the generic, continuous, and quasi-objective SF12 health measure. Our findings reveal strong and highly significant spatial dependencies and clusters. The strong and systematic county-level impact is comparable to an age effect on health of up to 30 years. Even 20 years after the peaceful German reunification, we detect a clear spatial East-West health pattern that equals an age impact on health of up to 10 life years.

Abstract

This paper makes use of Hierarchical Bayes Models to model and estimate spatial health effects. We focus on Germany, combining rich individual-level household panel data with administrative county¿level information to estimate spatial county-level health dependencies. As dependent variable, we use the generic, continuous, and quasi-objective SF12 health measure. Our findings reveal strong and highly significant spatial dependencies and clusters. The strong and systematic county-level impact is comparable to an age effect on health of up to 30 years. Even 20 years after the peaceful German reunification, we detect a clear spatial East-West health pattern that equals an age impact on health of up to 10 life years.

Themen: Surveymethodologie und Data-Science



JEL-Classification: C21;C11;I12;I14;I18
Keywords: spatial health effects, Hierarchical Bayes Models, Germany, SOEP, SF12
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