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Decomposing dimensions of mortality inequality

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  • Alexander, Monica

Abstract

This paper demonstrates how an existing mathematical decomposition technique can be used to understand mortality inequalities across populations. The use of Singular Value Decomposition to extract key mortality patterns by age and respective relative weights across populations is discussed. This method is demonstrated through decomposing mortality by US state to highlight the main dimensions of mortality inequalities, and finds: 1) that majority of differences in mortality age schedules across states in recent years is due to differences in young-adult mortality; and 2) that relatively high-mortality states have evolved in a fundamentally different way to low-mortality states over time. This decomposition approach is complementary to existing methods to describe and summarize mortality patterns, focusing on key dimensions of mortality differences.

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  • Alexander, Monica, 2022. "Decomposing dimensions of mortality inequality," SocArXiv uqwxj, Center for Open Science.
    • Handle: RePEc:osf:socarx:uqwxj
    DOI: 10.31219/osf.io/uqwxj
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    References listed on IDEAS

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    1. Samuel H. Preston & Jessica Y. Ho, 2009. "Low Life Expectancy in the United States: Is the Health Care System at Fault?," NBER Working Papers 15213, National Bureau of Economic Research, Inc.
    2. Janet Currie & Hannes Schwandt, 2016. "Mortality Inequality: The Good News from a County-Level Approach," Journal of Economic Perspectives, American Economic Association, vol. 30(2), pages 29-52, Spring.
    3. Samuel J. Clark, 2019. "A General Age-Specific Mortality Model With an Example Indexed by Child Mortality or Both Child and Adult Mortality," Demography, Springer;Population Association of America (PAA), vol. 56(3), pages 1131-1159, June.
    4. Joop de Beer, 2011. "A new relational method for smoothing and projecting age-specific fertility rates: TOPALS," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 24(18), pages 409-454.
    5. Monica Alexander & Emilio Zagheni & Magali Barbieri, 2017. "A Flexible Bayesian Model for Estimating Subnational Mortality," Demography, Springer;Population Association of America (PAA), vol. 54(6), pages 2025-2041, December.
    6. Carter, Lawrence R. & Lee, Ronald D., 1992. "Modeling and forecasting US sex differentials in mortality," International Journal of Forecasting, Elsevier, vol. 8(3), pages 393-411, November.
    7. Nan Li & Ronald Lee, 2005. "Coherent mortality forecasts for a group of populations: An extension of the lee-carter method," Demography, Springer;Population Association of America (PAA), vol. 42(3), pages 575-594, August.
    8. Catherine Ross & Ryan Masters & Robert Hummer, 2012. "Education and the Gender Gaps in Health and Mortality," Demography, Springer;Population Association of America (PAA), vol. 49(4), pages 1157-1183, November.
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