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Examining the Structure of Spatial Health Effects using Hierarchical Bayes Models

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  • Eibich, Peter
  • Ziebarth, Nicolas

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 31 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 9 life years.

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  • Eibich, Peter & Ziebarth, Nicolas, 2013. "Examining the Structure of Spatial Health Effects using Hierarchical Bayes Models," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79844, Verein für Socialpolitik / German Economic Association.
    • Handle: RePEc:zbw:vfsc13:79844
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    2. Mine Kühn & Christian Dudel & Tobias C. Vogt & Anna Oksuzyan, 2017. "Trends in gender differences in health and mortality at working ages among West and East Germans," MPIDR Working Papers WP-2017-009, Max Planck Institute for Demographic Research, Rostock, Germany.

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    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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