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An Analysis of the Relationship between U.S. State Level Carbon Dioxide Emissions and Health Care Expenditure

Author

Listed:
  • Nicholas Apergis

    () (Department of Banking and Financial Management, University of Piraeus, Greece)

  • Rangan Gupta

    () (Department of Economics, University of Pretoria)

  • Chi Keung Marco Lau

    () (Newcastle Business School, Northumbria University, UK)

  • Zinnia Mukherjee

    () (Department of Economics, Simmons College, USA)

Abstract

This paper is the first to provide an empirical analysis of the short run and long run effects of carbon dioxide emissions on health care spending across U.S. states. Accounting for the possibility of non-linearity in the data of the individual variables as well as in the relationship amongst the variables, the analysis estimated various statistical models to show that CO2 emissions increased health care expenditures. Using quantile regressions, the analysis displayed that the effect of CO2 emissions was stronger at the upper-end of the conditional distribution of health care expenditures. The results indicate the effect of CO2 emissions on health care was relatively stronger for states that spend higher amounts in health care expenditures. A key policy message that stems out of the empirical findings is that the health benefits associated with policies implemented to reduce CO2 emissions can more than pay for the costs of implementing these policies.

Suggested Citation

  • Nicholas Apergis & Rangan Gupta & Chi Keung Marco Lau & Zinnia Mukherjee, 2016. "An Analysis of the Relationship between U.S. State Level Carbon Dioxide Emissions and Health Care Expenditure," Working Papers 201618, University of Pretoria, Department of Economics.
  • Handle: RePEc:pre:wpaper:201618
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    health care expenditure; carbon dioxide emissions; panel cointegration; panel quantile regression;

    JEL classification:

    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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