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Measuring Health Inequality with Categorical Data: Some Regional Patterns

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  • Joan Costa-i-Font
  • Frank Cowell

Abstract

Much of the theoretical literature on inequality assumes that the equalisand is a cardinal variable like income or wealth. However, health status is generally measured as a categorical variable expressing a qualitative order. Traditional solutions involve reclassifying the variable by means of qualitative models and relying on inequality measures that are mean independent. We argue that the way status is conceptualized has important theoretical implications for measurement as well as for policy analysis. We also bring to the data a recently proposed approach to measuring self-reported health inequality that meets both rigorous and practical considerations. We draw upon the World Health Survey data to examine alternative pragmatic methods for making health inequality comparisons. Findings suggest significant differences in health inequality measurement and that regional and country patterns of inequality orderings do not coincide with any reasonable categorization of countries by health system organization.

Suggested Citation

  • Joan Costa-i-Font & Frank Cowell, 2013. "Measuring Health Inequality with Categorical Data: Some Regional Patterns," CESifo Working Paper Series 4427, CESifo.
  • Handle: RePEc:ces:ceswps:_4427
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    Cited by:

    1. Joan Costa-Font & Frank A. Cowell, 2022. "The measurement of health inequalities: does status matter?," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 20(2), pages 299-325, June.
    2. Paul Makdissi & Myra Yazbeck, 2014. "Robust Wagstaff Orderings of Distributions of Self-Reported Health Status," Discussion Papers Series 533, School of Economics, University of Queensland, Australia.
    3. Joan Costa‐Font & Frank A. Cowell & Belen Saenz de Miera, 2021. "Measuring pure health inequality and mobility during a health insurance expansion: Evidence from Mexico," Health Economics, John Wiley & Sons, Ltd., vol. 30(8), pages 1833-1848, August.
    4. Gunawan, David & Griffiths, William E. & Chotikapanich, Duangkamon, 2018. "Bayesian inference for health inequality and welfare using qualitative data," Economics Letters, Elsevier, vol. 162(C), pages 76-80.
    5. Valérie Bérenger & Jacques Silber, 2022. "On the Measurement of Happiness and of its Inequality," Journal of Happiness Studies, Springer, vol. 23(3), pages 861-902, March.
    6. Joan Costa-Font & Cristina Hernandez-Quevedo & Azusa Sato, 2018. "A Health ‘Kuznets’ Curve’? Cross-Sectional and Longitudinal Evidence on Concentration Indices’," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 136(2), pages 439-452, April.

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    More about this item

    Keywords

    health inequality; categorical data; health surveys; upward status; downward status;
    All these keywords.

    JEL classification:

    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement
    • H23 - Public Economics - - Taxation, Subsidies, and Revenue - - - Externalities; Redistributive Effects; Environmental Taxes and Subsidies
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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