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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.

Suggested Citation

  • Peter Eibich & Nicolas R. Ziebarth, 2013. "Examining the Structure of Spatial Health Effects in Germany Using Hierarchical Bayes Models," SOEPpapers on Multidisciplinary Panel Data Research 620, DIW Berlin, The German Socio-Economic Panel (SOEP).
  • Handle: RePEc:diw:diwsop:diw_sp620
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    Cited by:

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    2. Dirk Göpffarth & Thomas Kopetsch & Hendrik Schmitz, 2016. "Determinants of Regional Variation in Health Expenditures in Germany," Health Economics, John Wiley & Sons, Ltd., vol. 25(7), pages 801-815, July.
    3. Salm, Martin & Wübker, Ansgar, 2020. "Sources of regional variation in healthcare utilization in Germany," Journal of Health Economics, Elsevier, vol. 69(C).
    4. Lorenz, Olga & Goerke, Laszlo, 2015. "Commuting and Sickness Absence," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113173, Verein für Socialpolitik / German Economic Association.
    5. Salm, Martin & Wübker, Ansgar, 2017. "Causes of regional variation in healthcare utilization in Germany," Ruhr Economic Papers 675, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    6. Ziebarth, Nicolas R., 2017. "Social Insurance and Health," IZA Discussion Papers 10918, Institute of Labor Economics (IZA).
    7. Alexander Silbersdorff & Julia Lynch & Stephan Klasen & Thomas Kneib, 2017. "Reconsidering the Income-Illness Relationship using Distributional Regression: An Application to Germany," Courant Research Centre: Poverty, Equity and Growth - Discussion Papers 231, Courant Research Centre PEG.
    8. Luca Grassetti & Laura Rizzi, 2019. "The determinants of individual health care expenditures in the Italian region of Friuli Venezia Giulia: evidence from a hierarchical spatial model estimation," Empirical Economics, Springer, vol. 56(3), pages 987-1009, March.
    9. Jaesang Sung & Qihua Qiu & Will Davis & Rusty Tchernis, 2022. "Design and Application of an Area-Level Suicide Risk Index with Spatial Correlation," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 161(1), pages 77-104, May.
    10. Linus Holtermann & Christian Hundt, 2018. "Hierarchically structured determinants and phase related patterns of economic resilience. An empirical case study for European regions," Working Papers on Innovation and Space 2018-02, Philipps University Marburg, Department of Geography.
    11. Batabyal, Amitrajeet & Beladi, Hamid, 2021. "Health Interventions in a Poor Region and Resilience in the Presence of a Pandemic," MPRA Paper 112159, University Library of Munich, Germany, revised 11 Jan 2022.
    12. Katharina Schley, 2018. "Health care service provision in Europe and regional diversity: a stochastic metafrontier approach," Health Economics Review, Springer, vol. 8(1), pages 1-11, December.
    13. Rouven Edgar Haschka & Katharina Schley & Helmut Herwartz, 2020. "Provision of health care services and regional diversity in Germany: insights from a Bayesian health frontier analysis with spatial dependencies," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 21(1), pages 55-71, February.
    14. Moscone, Francesco & Skinner, Jonathan & Tosetti, Elisa & Yasaitis, Laura, 2019. "The association between medical care utilization and health outcomes: A spatial analysis," Regional Science and Urban Economics, Elsevier, vol. 77(C), pages 306-314.
    15. Wang, Shaobin, 2020. "Spatial patterns and social-economic influential factors of population aging: A global assessment from 1990 to 2010," Social Science & Medicine, Elsevier, vol. 253(C).
    16. Holtermann, Linus & Hundt, Christian, 2018. "Hierarchically structured determinants and phase-related patterns of economic resilience – An empirical case study for European regions," MPRA Paper 88359, University Library of Munich, Germany.
    17. 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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    More about this item

    Keywords

    Spatial health effects; Hierarchical Bayes Models; Germany; SOEP; SF12;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • I14 - Health, Education, and Welfare - - Health - - - Health and Inequality
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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