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Heterogeneity in the Effect of Common Shocks on Healthcare Expenditure Growth

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  • Katharina Hauck
  • Xiaohui Zhang
Abstract
Healthcare expenditure growth is affected by important unobserved common shocks such as technological innovation, changes in sociological factors, shifts in preferences, and the epidemiology of diseases. While common factors impact in principle all countries, their effect is likely to differ across countries. To allow for unobserved heterogeneity in the effects of common shocks, we estimate a panel data model of healthcare expenditure growth in 34 OECD countries over the years 1980 to 2012, where the usual fixed or random effects are replaced by a multifactor error structure. We address model uncertainty with Bayesian model averaging, to identify a small set of robust expenditure drivers from 43 potential candidates. We establish 16 significant drivers of healthcare expenditure growth, including growth in GDP per capita and in insurance premiums, changes in financing arrangements and some institutional characteristics, expenditures on pharmaceuticals, population ageing, costs of health administration, and inpatient care. Our approach allows us to provide robust evidence to policy makers on the drivers that were most strongly associated with the growth in healthcare expenditures over the past 32 years. Copyright © 2016 John Wiley & Sons, Ltd.

Suggested Citation

  • Katharina Hauck & Xiaohui Zhang, 2016. "Heterogeneity in the Effect of Common Shocks on Healthcare Expenditure Growth," Health Economics, John Wiley & Sons, Ltd., vol. 25(9), pages 1090-1103, September.
    • Handle: RePEc:wly:hlthec:v:25:y:2016:i:9:p:1090-1103
    DOI: 10.1002/hec.3329
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    References listed on IDEAS

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    2. Fengping Tian & Jiti Gao & Ke Yang, 2018. "A quantile regression approach to panel data analysis of health‐care expenditure in Organisation for Economic Co‐operation and Development countries," Health Economics, John Wiley & Sons, Ltd., vol. 27(12), pages 1921-1944, December.
    3. Jochen Hartwig & Jan-Egbert Sturm, 2018. "Testing the Grossman model of medical spending determinants with macroeconomic panel data," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 19(8), pages 1067-1086, November.
    4. Giovanni Forchini & Bin Jiang & Bin Peng, 2015. "Consistent Estimation in Large Heterogeneous Panels with Multifactor Structure and Endogeneity," School of Economics Discussion Papers 0315, School of Economics, University of Surrey.
    5. Isabel Casas & Jiti Gao & Bin Peng & Shangyu Xie, 2021. "Time‐varying income elasticities of healthcare expenditure for the OECD and Eurozone," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(3), pages 328-345, April.
    6. Fei Liu & Jiti Gao & Yanrong Yang, 2020. "Time-Varying Panel Data Models with an Additive Factor Structure," Monash Econometrics and Business Statistics Working Papers 42/20, Monash University, Department of Econometrics and Business Statistics.
    7. Kaan Celebi & Jochen Hartwig & Anna Pauliina Sandqvist, 2024. "Baumol's Cost Disease in Acute vs. Long-term Care - Do the Differences Loom Large?," Chemnitz Economic Papers 062, Department of Economics, Chemnitz University of Technology.
    8. Rezwanul Hasan Rana & Khorshed Alam & Jeff Gow, 2020. "The Impact of Immigration on Public and Out-of-Pocket Health Expenditure in OECD Countries," Journal of International Migration and Integration, Springer, vol. 21(2), pages 485-508, June.
    9. Mujaheed Shaikh & Afschin Gandjour, 2019. "Pharmaceutical expenditure and gross domestic product: Evidence of simultaneous effects using a two‐step instrumental variables strategy," Health Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 101-122, January.
    10. Elisabet Rodriguez Llorian & Janelle Mann, 2022. "Exploring the technology–healthcare expenditure nexus: a panel error correction approach," Empirical Economics, Springer, vol. 62(6), pages 3061-3086, June.

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