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Medical consumption over the life-cycle

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  • Juergen Jung
  • Chung Tran

Abstract

We investigate the association between age and medical spending in the U.S. using data from the Medical Expenditure Panel Survey. We estimate a partially linear seminonparametric model and construct “pure” life-cycle profiles of health spending simultaneously controlling for time effects (i.e., institutional changes and business cycles effects) and cohort effects (i.e., generation specific conditions). We find that time and cohort effects together introduce a significant estimation bias into predictions of health expenditures per age group, especially for individuals older than 60 years. The estimation bias introduced by cohort effects increases monotonically with age while the bias due to time effects is not significant. The overall effect of Medicare on the cohort and time effects biases is negligible. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Juergen Jung & Chung Tran, 2014. "Medical consumption over the life-cycle," Empirical Economics, Springer, vol. 47(3), pages 927-957, November.
    • Handle: RePEc:spr:empeco:v:47:y:2014:i:3:p:927-957
    DOI: 10.1007/s00181-013-0774-6
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    Cited by:

    1. Sheng-Ti Hung & Kevin X.D. Huang & Hui He, 2013. "Substituting Leisure for Health Expenditure: A General Equilibrium-Based Empirical Investigation," 2013 Meeting Papers 1310, Society for Economic Dynamics.
    2. Juergen Jung & Chung Tran, 2016. "Market Inefficiency, Insurance Mandate and Welfare: U.S. Health Care Reform 2010," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 20, pages 132-159, April.
    3. FUKAI Taiyo & ICHIMURA Hidehiko & KANAZAWA Kyogo, 2018. "Quantifying Health Shocks over the Life Cycle," Discussion papers 18014, Research Institute of Economy, Trade and Industry (RIETI).
    4. Juergen Jung, 2022. "Estimating transition probabilities between health states using US longitudinal survey data," Empirical Economics, Springer, vol. 63(2), pages 901-943, August.
    5. Eric French & Elaine Kelly & Svetlana Pashchenko & Ponpoje Porapakkarm, 2016. "Medical Spending in the US: Facts from the Medical Expenditure Panel Survey Data Set," Fiscal Studies, Institute for Fiscal Studies, vol. 37, pages 689-716, September.
    6. Hui He & Kevin X.D. Huang & Lei Ning, 2021. "Why Do Americans Spend So Much More On Health Care Than Europeans?," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 62(4), pages 1363-1399, November.
    7. Kelly, Mark & Kuhn, Michael, 2022. "Congestion in a public health service: A macro approach," Journal of Macroeconomics, Elsevier, vol. 74(C).
    8. Shuyun May Li & Solmaz Moslehi & Siew Ling Yew, 2016. "Public–private mix of health expenditure: A political economy and quantitative analysis," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 49(2), pages 834-866, May.
    9. Juergen Jung & Chung Tran, 2016. "Market Inefficiency, Insurance Mandate and Welfare: U.S. Health Care Reform 2010," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 20, pages 132-159, April.

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    More about this item

    Keywords

    Life-cycle profiles; Time and cohort effects; Partial linear seminonparametric models; Pseudo panels; Medical Expenditure Panel Survey (MEPS); I10; I11; C14; C23; D12; D91; J10;
    All these keywords.

    JEL classification:

    • I10 - Health, Education, and Welfare - - Health - - - General
    • I11 - Health, Education, and Welfare - - Health - - - Analysis of Health Care Markets
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
    • J10 - Labor and Demographic Economics - - Demographic Economics - - - General

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