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Technology Growth and Expenditure Growth in Health Care

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  • Amitabh Chandra
  • Jonathan S. Skinner

Abstract

In the United States, health care technology has contributed to rising survival rates, yet health care spending relative to GDP has also grown more rapidly than in any other country. We develop a model of patient demand and supplier behavior to explain these parallel trends in technology growth and cost growth. We show that health care productivity depends on the heterogeneity of treatment effects across patients, the shape of the health production function, and the cost structure of procedures such as MRIs with high fixed costs and low marginal costs. The model implies a typology of medical technology productivity: (I) highly cost-effective "home run" innovations with little chance of overuse, such as anti-retroviral therapy for HIV, (II) treatments highly effective for some but not for all (e.g. stents), and (III) "gray area" treatments with uncertain clinical value such as ICU days among chronically ill patients. Not surprisingly, countries adopting Category I and effective Category II treatments gain the greatest health improvements, while countries adopting ineffective Category II and Category III treatments experience the most rapid cost growth. Ultimately, economic and political resistance in the U.S. to ever-rising tax rates will likely slow cost growth, with uncertain effects on technology growth.

Suggested Citation

  • Amitabh Chandra & Jonathan S. Skinner, 2011. "Technology Growth and Expenditure Growth in Health Care," NBER Working Papers 16953, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:16953
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    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Medical expenditure and technology growth
      by Economic Logician in Economic Logic on 2011-05-27 19:57:00

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    Cited by:

    1. Wydra, Sven, 2015. "Challenges for technology diffusion policy to achieve socio-economic goals," Technology in Society, Elsevier, vol. 41(C), pages 76-90.
    2. Freedman, Seth & Lin, Haizhen & Simon, Kosali, 2015. "Public health insurance expansions and hospital technology adoption," Journal of Public Economics, Elsevier, vol. 121(C), pages 117-131.
    3. 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.
    4. Levaggi, R. & Moretto, M. & Pertile, P., 2012. "Static and dynamic efficiency of irreversible health care investments under alternative payment rules," Journal of Health Economics, Elsevier, vol. 31(1), pages 169-179.
    5. Jeffrey Clemens & Joshua D. Gottlieb, 2014. "Do Physicians' Financial Incentives Affect Medical Treatment and Patient Health?," American Economic Review, American Economic Association, vol. 104(4), pages 1320-1349, April.
    6. Astolfi, Roberto & Lorenzoni, Luca & Oderkirk, Jillian, 2012. "Informing policy makers about future health spending: A comparative analysis of forecasting methods in OECD countries," Health Policy, Elsevier, vol. 107(1), pages 1-10.
    7. Proksch, Dorian & Busch-Casler, Julia & Haberstroh, Marcus Max & Pinkwart, Andreas, 2019. "National health innovation systems: Clustering the OECD countries by innovative output in healthcare using a multi indicator approach," Research Policy, Elsevier, vol. 48(1), pages 169-179.

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    JEL classification:

    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • I1 - Health, Education, and Welfare - - Health
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior

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