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Examining the Structure of Spatial Health Effects in Germany Using Hierarchical Bayes Models

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  • Eibich, Peter
  • Ziebarth, Nicolas
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.

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  • Eibich, Peter & Ziebarth, Nicolas, 2014. "Examining the Structure of Spatial Health Effects in Germany Using Hierarchical Bayes Models," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 49, pages 305-320.
    • Handle: RePEc:zbw:espost:106944
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    4. 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.
    5. 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.
    6. Helmut Herwartz & Christoph Strumann, 2024. "Too many cooks could spoil the broth: choice overload and the provision of ambulatory health care," International Journal of Health Economics and Management, Springer, vol. 24(3), pages 357-373, September.
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    8. 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.
    9. 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.
    10. 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.
    11. Nicolas R. Ziebarth, 2018. "Social Insurance and Health," Contributions to Economic Analysis, in: Health Econometrics, volume 127, pages 57-84, Emerald Group Publishing Limited.
    12. 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.
    13. 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.
    14. 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.
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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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