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Measuring the compression of mortality

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  • Väinö Kannisto

    (Max Planck Institute for Demographic Research)

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    Abstract

    Compression of mortality is measured here in four ways: (1) by standard deviation of the age at death above the mode; (2) by standard deviation of the age at death in the highest quartile; (3) by the inter-quartile range; and (4) by the shortest age interval in which a given proportion of deaths take place. The two first-mentioned are directed at old ages while the other two measure compression over the entire age range. The fourth alternative is recommended as the most suitable for general use and offers several variations, called the C-family of compression indicators. Applied to historical and modern populations, all four measures show convincingly that the secular transition from high to low mortality has been accompanied by general and massive compression of mortality. In recent decades, however, this development has come close to stagnation even when life expectancy continues to increase. This has happened at a level where compression is still so incomplete that the shortest age interval in which 90 percent of deaths occur, is more than 35 years. It seems unrealistic to expect human mortality ever to be compressed into so narrow an age interval that the survival curve would be even approximately rectangular. It is considered useful to monitor changes in the compression of mortality because the indicators describe relevant aspects of the length of life and may acquire new significance as indicators of population heterogeneity.

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    Bibliographic Info

    Article provided by Max Planck Institute for Demographic Research, Rostock, Germany in its journal Demographic Research.

    Volume (Year): 3 (2000)
    Issue (Month): 6 (September)
    Pages:

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    Handle: RePEc:dem:demres:v:3:y:2000:i:6

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    Web page: http://www.demogr.mpg.de/

    Related research

    Keywords: length of life; mortality; mortality analysis;

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    Cited by:
    1. France Meslé & Jacques Vallin, 2002. "Montée de l'espérance de vie et concentration des âges au décès," Working Papers 108, Institut National d'Études Démographiques (INED).
    2. Virginia Zarulli & Domantas Jasilionis & Dmitri A. Jdanov, 2012. "Changes in educational differentials in old-age mortality in Finland and Sweden between 1971-1975 and 1996-2000," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 26(19), pages 489-510, May.
    3. Claudia Nau & Glenn Firebaugh, 2012. "A New Method for Determining Why Length of Life is More Unequal in Some Populations Than in Others," Demography, Springer, vol. 49(4), pages 1207-1230, November.
    4. Pierre Pestieau & Grégory Ponthiere, 2012. "On the Policy Implications of Changing Longevity," CESifo Working Paper Series 3926, CESifo Group Munich.
    5. Shripad Tuljapurkar & Ryan D. Edwards, 2011. "Variance in death and its implications for modeling and forecasting mortality," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 24(21), pages 497-526, March.
    6. Jean-Marie Robine & Siu Lan Karen Cheung & Shiro Horiuchi, 2010. "Arthur Roger Thatcher's contributions to longevity research," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 22(18), pages 539-548, March.
    7. Alyson Raalte & Hal Caswell, 2013. "Perturbation Analysis of Indices of Lifespan Variability," Demography, Springer, vol. 50(5), pages 1615-1640, October.
    8. Nadine Ouellette & Robert Bourbeau, 2011. "Changes in the age-at-death distribution in four low mortality countries: A nonparametric approach," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 25(19), pages 595-628, September.

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