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Income and Technology as Drivers of Australian Healthcare Expenditures

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  • Xiaohui You
  • Albert A. Okunade

Abstract

The roles of income and technology as the major determinants of aggregate healthcare expenditure (HEXP) continue to interest economists and health policy researchers. Concepts and measures of medical technologies remain complex; however, income (on the demand side) and technology (on the supply side) are important drivers of HEXP. This paper presents analysis of Australia's HEXP, using time‐series econometrics modeling techniques applied to 1971–2011 annual aggregate data. Our work fills two important gaps in the literature. First, we model the determinants of Australia's HEXP using the latest and longest available data series. Second, this novel study investigates several alternative technology proxies (input and output measures), including economy‐wide research and development expenditures, hospital research expenditures, mortality rate, and two technology indexes based on medical devices. We then apply the residual component method and the technology proxy approach to quantify the technology effects on HEXP. Our empirical results suggest that Australian aggregate healthcare is a normal good and a technical necessity with the income elasticity estimates ranging from 0.51 to 0.97, depending on the model. The estimated technology effects on HEXP falling in the 0.30–0.35 range and mimicking those in the literature using the US data, reinforce the global spread of healthcare technology. Copyright © 2016 John Wiley & Sons, Ltd.

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  • Xiaohui You & Albert A. Okunade, 2017. "Income and Technology as Drivers of Australian Healthcare Expenditures," Health Economics, John Wiley & Sons, Ltd., vol. 26(7), pages 853-862, July.
    • Handle: RePEc:wly:hlthec:v:26:y:2017:i:7:p:853-862
    DOI: 10.1002/hec.3403
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    3. Barkat, Karim & Sbia, Raschid & Maouchi, Youcef, 2019. "Empirical evidence on the long and short run determinants of health expenditure in the Arab world," The Quarterly Review of Economics and Finance, Elsevier, vol. 73(C), pages 78-87.
    4. Caravaggio, Nicola & Resce, Giuliano, 2023. "Enhancing Healthcare Cost Forecasting: A Machine Learning Model for Resource Allocation in Heterogeneous Regions," Economics & Statistics Discussion Papers esdp23090, University of Molise, Department of Economics.
    5. Nor Aziah Abd Kadir & Nur Fakhzan Marwan & Adibah Hussin & Rosmah Nizam & Fazreena Mansor, 2022. "Long Run Analysis between Climate Change, Socio-Economic Factors and Technology on Health Expenditure in Malaysia," International Journal of Human Resource Studies, Macrothink Institute, vol. 12(2), pages 6589-6589, December.
    6. Wenkang Ma & Ana Timóteo & Vanessa Ribeiro & Céu Mateus & Julian Perelman, 2024. "Contribution of high-technology procedures to public healthcare expenditures: the case of ischemic heart disease in Portugal, 2002–2015," International Journal of Health Economics and Management, Springer, vol. 24(3), pages 419-437, September.
    7. 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.
    8. Boxebeld, Sander & Geijsen, Tom & Tuit, Charlotte & Exel, Job van & Makady, Amr & Maes, Laurence & van Agthoven, Michel & Mouter, Niek, 2024. "Public preferences for the allocation of societal resources over different healthcare purposes," Social Science & Medicine, Elsevier, vol. 341(C).
    9. Chak Hung Jack Cheng & Nopphol Witvorapong, 2021. "Health care policy uncertainty, real health expenditures and health care inflation in the USA," Empirical Economics, Springer, vol. 60(4), pages 2083-2103, April.
    10. Anne Mason & Idaira Rodriguez Santana & María José Aragón & Nigel Rice & Martin Chalkley & Raphael Wittenberg & Jose-Luis Fernandez, 2019. "Drivers of health care expenditure: Final report," Working Papers 169cherp, Centre for Health Economics, University of York.

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