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Analyzing Regional Variation in Health Care Utilization Using (Rich) Household Microdata

Author

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  • Eibich, P.
  • Ziebarth, N.

Abstract

This paper exploits rich SOEP microdata to analyze state-level variation in health care utilization in Germany. Unlike most studies in the field of the Small Area Variation (SAV) literature, our approach allows us to net out a large array of individual-level and state-level factors that may contribute to the geographic variation in health care utilization. The raw data suggest that state-level hospitalization rates vary from 65 percent to 165 percent of the national mean. Ambulatory doctor visits range from 90 percent to 120 percent of the national mean. Interestingly, in the former GDR states doctor visit rates are significantly below the national mean, while hospitalization rates lie above the national mean. The significant state-level differences vanish once we control for individual-level socio-economic characteristics, the respondents’ health status, their health behavior as well as supply-side state-level factors.

Suggested Citation

  • Eibich, P. & Ziebarth, N., 2013. "Analyzing Regional Variation in Health Care Utilization Using (Rich) Household Microdata," Health, Econometrics and Data Group (HEDG) Working Papers 13/12, HEDG, c/o Department of Economics, University of York.
    • Handle: RePEc:yor:hectdg:13/12
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    Cited by:

    1. Salm, Martin & Wübker, Ansgar, 2020. "Sources of regional variation in healthcare utilization in Germany," Journal of Health Economics, Elsevier, vol. 69(C).
    2. 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.
    3. 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.
    4. Lavergne, Miriam Ruth & Barer, Morris & Law, Michael R. & Wong, Sabrina T. & Peterson, Sandra & McGrail, Kimberlyn, 2016. "Examining regional variation in health care spending in British Columbia, Canada," Health Policy, Elsevier, vol. 120(7), pages 739-748.
    5. Natascha Hainbach & Christoph Halbmeier & Timo Schmid & Carsten Schröder, 2019. "A Practical Guide for the Computation of Domain-Level Estimates with the Socio-Economic Panel (and Other Household Surveys)," SOEPpapers on Multidisciplinary Panel Data Research 1055, DIW Berlin, The German Socio-Economic Panel (SOEP).
    6. Danny Wende & Alexander Karmann & Ines Weinhold, 2024. "Deprivation as a fundamental cause of morbidity and reduced life expectancy: an observational study using German statutory health insurance data," International Journal of Health Economics and Management, Springer, vol. 24(2), pages 257-277, June.
    7. Mohan, Gretta & Nolan, Anne & Lyons, Seán, 2019. "An investigation of the effect of accessibility to General Practitioner services on healthcare utilisation among older people," Social Science & Medicine, Elsevier, vol. 220(C), pages 254-263.
    8. Simon Spika & Friedrich Breyer, 2020. "Domain-specific effects of physical activity on the demand for physician visits," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 65(5), pages 583-591, June.
    9. Schurer, Stefanie, 2017. "Bouncing back from health shocks: Locus of control and labor supply," Journal of Economic Behavior & Organization, Elsevier, vol. 133(C), pages 1-20.
    10. 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.
    11. Omar B. Da’ar & Farah Kalmey, 2023. "The level of countries’ preparedness to health risks during Covid-19 and pre-pandemic: the differential response to health systems building blocks and socioeconomic indicators," Health Economics Review, Springer, vol. 13(1), pages 1-14, December.
    12. Danny Wende, 2019. "Spatial risk adjustment between health insurances: using GWR in risk adjustment models to conserve incentives for service optimisation and reduce MAUP," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 20(7), pages 1079-1091, September.
    13. Kollerup, Anna & Wadmann, Sarah & Bek, Toke & Kjellberg, Jakob, 2022. "National clinical guidelines and treatment centralization do not guarantee consistency in healthcare delivery. A mixed-methods study of wet age-related macular degeneration treatment in Denmark," Health Policy, Elsevier, vol. 126(12), pages 1291-1302.
    14. Torrini, Irene & Grassetti, Luca & Rizzi, Laura, 2023. "Under-spending, over-spending or substitution among services? Spatial patterns of unexplained shares of health care expenditures," Health Policy, Elsevier, vol. 137(C).

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    Keywords

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    JEL classification:

    • 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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