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Getting the Right Tail Right: Modeling Tails of Health Expenditure Distributions

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  • Martin Karlsson
  • Yulong Wang
  • Nicolas R. Ziebarth

Abstract

Health expenditure data almost always include extreme values, implying that the underlying distribution has heavy tails. This may result in infinite variances as well as higher-order moments and bias the commonly used least squares methods. To accommodate extreme values, we propose an estimation method that recovers the right tail of health expenditure distributions. It extends the popular two-part model to develop a novel three-part model. We apply the proposed method to claims data from one of the biggest German private health insurers. Our findings show that the estimated age gradient in health care spending differs substantially from the standard least squares method.

Suggested Citation

  • Martin Karlsson & Yulong Wang & Nicolas R. Ziebarth, 2023. "Getting the Right Tail Right: Modeling Tails of Health Expenditure Distributions," NBER Working Papers 31444, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:31444
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    Cited by:

    1. Kurt Lavetti & Thomas DeLeire & Nicolas R. Ziebarth, 2023. "How do low‐income enrollees in the Affordable Care Act marketplaces respond to cost‐sharing?," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 90(1), pages 155-183, March.
    2. Avdic, Daniel & Decker, Simon & Karlsson, Martin & Salm, Martin, 2024. "No-claim refunds and healthcare use," Journal of Public Economics, Elsevier, vol. 230(C).

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    More about this item

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • I10 - Health, Education, and Welfare - - Health - - - General
    • I13 - Health, Education, and Welfare - - Health - - - Health Insurance, Public and Private

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