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Mortality Comparisons 'At a Glance': A Mortality Concentration Curve and Decomposition Analysis for India

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  • Creedy, John
  • Subramanian, S.

Abstract

This paper uses the concept of the M-Curve, which plots the cumulative proportion of deaths against the corresponding cumulative proportion of the population (arranged in ascending order of age), and associated measures, to examine mortality experience in India. A feature of the M-curve is that it can be combined with an explicit value judgement (an aversion to early deaths) in order to make welfare-loss comparisons. Empirical comparisons over time, and between regions and genders, are made. Furthermore, in order to provide additional perspective, selective results for the UK and New Zealand are reported. It is also shown how the M-curve concept can be used to separate the contributions to overall mortality of changes over time (or differences between population groups) to the population age distribution and age-specific mortality rates.

Suggested Citation

  • Creedy, John & Subramanian, S., 2022. "Mortality Comparisons 'At a Glance': A Mortality Concentration Curve and Decomposition Analysis for India," Working Paper Series 22007, Victoria University of Wellington, Chair in Public Finance.
    • Handle: RePEc:vuw:vuwcpf:22007
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    References listed on IDEAS

    as
    1. Minu Philip & Debraj Ray & S. Subramanian, 2021. "Decoding India's Low Covid-19 Case Fatality Rate," Journal of Human Development and Capabilities, Taylor & Francis Journals, vol. 22(1), pages 27-51, January.
    2. Christian Dudel & Timothy Riffe & Enrique Acosta & Alyson A. van Raalte & Cosmo Strozza & Mikko Myrskylä, 2020. "Monitoring trends and differences in COVID-19 case-fatality rates using decomposition methods: contributions of age structure and age-specific fatality," MPIDR Working Papers WP-2020-020, Max Planck Institute for Demographic Research, Rostock, Germany.
    3. Johan Fellman, 2012. "Estimation of Gini coefficients using Lorenz curves," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 1(2), pages 1-3.
    4. Anthony Shorrocks, 2013. "Decomposition procedures for distributional analysis: a unified framework based on the Shapley value," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 11(1), pages 99-126, March.
    5. repec:bla:econom:v:50:y:1983:i:197:p:3-17 is not listed on IDEAS
    6. Sen, Amartya, 1973. "On Economic Inequality," OUP Catalogue, Oxford University Press, number 9780198281931.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Mortality Curve; Mortality-inefficiency measure; Crude Death Rate; Lorenz Curve; Age-distribution of population; Age-specific death rates; M-Curve comparisons; Decomposition; Age and fatality effects; Decomposition; Mean and dispersion effects;
    All these keywords.

    JEL classification:

    • D30 - Microeconomics - - Distribution - - - General
    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement
    • I31 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - General Welfare, Well-Being
    • J17 - Labor and Demographic Economics - - Demographic Economics - - - Value of Life; Foregone Income

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