IDEAS home Printed from https://ideas.repec.org/p/aah/create/2016-14.html
   My bibliography  Save this paper

Modelling Socio-Economic Differences in the Mortality of Danish Males Using a New Affluence Index

Author

Listed:
  • Andrew J.G. Cairns

    (Maxwell Institute for Mathematical Sciences and Heriot-Watt University 4AS, United Kingdom. E-mail: A.J.G.Cairns@hw.ac.uk)

  • Malene Kallestrup-Lamb

    (Aarhus University and CREATES)

  • Carsten P.T. Rosenskjold

    (Aarhus University and CREATES)

  • David Blake

    (Pensions Institute, Cass Business School, City University of London)

  • Kevin Dowd

    (Durham University Business School)

Abstract

We investigate and model how the mortality of Danish males aged 55-94 has changed over the period 1985-2012. We divide the population into ten socio-economic subgroups using a new measure of affluence that combines wealth and income reported on the Statistics Denmark national register database. The affluence index, in combination with sub-group lockdown at age 67, is shown to provide consistent sub-group rankings based on crude death rates across all ages and over all years in a way that improves significantly on previous studies that have focused on life expectancy. The gap between the most and least affluent is confirmed to be widest at younger ages and has widened over time. We introduce a new multi-population mortality model that fits the historical mortality data very well and generates smoothed death rates that can be used to model a larger number of smaller sub-groups than has been previously possible without losing the essential character of the raw data. The model produces bio-demographically reasonable forecasts of mortality rates that preserve the sub-group rankings at all ages. It also satisfies reasonableness criteria related to the term structure of correlations across ages and over time through consideration of future death and survival rates.

Suggested Citation

  • Andrew J.G. Cairns & Malene Kallestrup-Lamb & Carsten P.T. Rosenskjold & David Blake & Kevin Dowd, 2016. "Modelling Socio-Economic Differences in the Mortality of Danish Males Using a New Affluence Index," CREATES Research Papers 2016-14, Department of Economics and Business Economics, Aarhus University.
  • Handle: RePEc:aah:create:2016-14
    as

    Download full text from publisher

    File URL: https://repec.econ.au.dk/repec/creates/rp/16/rp16_14.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cristia, Julian P., 2009. "Rising mortality and life expectancy differentials by lifetime earnings in the United States," Journal of Health Economics, Elsevier, vol. 28(5), pages 984-995, September.
    2. 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.
    3. Cristia, Julian P., 2009. "Rising mortality and life expectancy differentials by lifetime earnings in the United States," Journal of Health Economics, Elsevier, vol. 28(5), pages 984-995, September.
    4. Kleinow, Torsten, 2015. "A common age effect model for the mortality of multiple populations," Insurance: Mathematics and Economics, Elsevier, vol. 63(C), pages 147-152.
    5. Andrew J. G. Cairns & David Blake & Kevin Dowd & Amy R. Kessler, 2016. "Phantoms never die: living with unreliable population data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(4), pages 975-1005, October.
    6. Andrew Hunt & David Blake, 2014. "A General Procedure for Constructing Mortality Models," North American Actuarial Journal, Taylor & Francis Journals, vol. 18(1), pages 116-138.
    7. Arnold-Gaille, Séverine & Sherris, Michael, 2016. "International Cause-Specific Mortality Rates: New Insights From A Cointegration Analysis," ASTIN Bulletin, Cambridge University Press, vol. 46(1), pages 9-38, January.
    8. Guy Coughlan & Marwa Khalaf-Allah & Yijing Ye & Sumit Kumar & Andrew Cairns & David Blake & Kevin Dowd, 2011. "Longevity Hedging 101," North American Actuarial Journal, Taylor & Francis Journals, vol. 15(2), pages 150-176.
    9. Andrew J. G. Cairns & David Blake & Kevin Dowd, 2006. "A Two‐Factor Model for Stochastic Mortality with Parameter Uncertainty: Theory and Calibration," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 73(4), pages 687-718, December.
    10. 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.
    11. Andrew Cairns & David Blake & Kevin Dowd & Guy Coughlan & David Epstein & Alen Ong & Igor Balevich, 2009. "A Quantitative Comparison of Stochastic Mortality Models Using Data From England and Wales and the United States," North American Actuarial Journal, Taylor & Francis Journals, vol. 13(1), pages 1-35.
    12. Chapman, Kenneth S & Hariharan, Govind, 1996. "Do Poor People Have a Stronger Relationship between Income and Mortality Than the Rich? Implications of Panel Data for Health-Health Analysis," Journal of Risk and Uncertainty, Springer, vol. 12(1), pages 51-63, January.
    13. Plat, Richard, 2009. "On stochastic mortality modeling," Insurance: Mathematics and Economics, Elsevier, vol. 45(3), pages 393-404, December.
    14. Andrew J.G. Cairns & Kevin Dowd & David Blake & Guy D. Coughlan, 2014. "Longevity hedge effectiveness: a decomposition," Quantitative Finance, Taylor & Francis Journals, vol. 14(2), pages 217-235, February.
    15. McDonough, P. & Duncan, G.J. & Williams, D. & House, J., 1997. "Income dynamics and adult mortality in the United States, 1972 through 1989," American Journal of Public Health, American Public Health Association, vol. 87(9), pages 1476-1483.
    16. Isaac Sasson, 2016. "Trends in Life Expectancy and Lifespan Variation by Educational Attainment: United States, 1990–2010," Demography, Springer;Population Association of America (PAA), vol. 53(2), pages 269-293, April.
    17. Jarner, Søren Fiig & Kryger, Esben Masotti, 2011. "Modelling Adult Mortality in Small Populations: The Saint Model," ASTIN Bulletin, Cambridge University Press, vol. 41(2), pages 377-418, November.
    18. Cairns, Andrew J.G. & Blake, David & Dowd, Kevin & Coughlan, Guy D. & Khalaf-Allah, Marwa, 2011. "Bayesian Stochastic Mortality Modelling for Two Populations," ASTIN Bulletin, Cambridge University Press, vol. 41(1), pages 29-59, May.
    19. Barry P. Bosworth & Kathleen Burke, 2014. "Differential Mortality and Retirement Benefits in the Health and Retirement Study," Working Papers, Center for Retirement Research at Boston College wp2014-4, Center for Retirement Research.
    20. Coughlan, Guy & Khalaf-Allah, Marwa & Ye, Yijing & Kumar, Sumit & Cairns, Andrew & Blake, David & Dowd, Kevin, 2011. "Longevity hedging 101: A framework for longevity basis risk analysis and hedge effectiveness," MPRA Paper 35743, University Library of Munich, Germany.
    21. Kleinow, Torsten & Cairns, Andrew J.G., 2013. "Mortality and smoking prevalence: An empirical investigation in ten developed countries," British Actuarial Journal, Cambridge University Press, vol. 18(2), pages 452-466, July.
    22. Andrés Villegas & Steven Haberman, 2014. "On the Modeling and Forecasting of Socioeconomic Mortality Differentials: An Application to Deprivation and Mortality in England," North American Actuarial Journal, Taylor & Francis Journals, vol. 18(1), pages 168-193.
    23. Kevin Dowd & Andrew Cairns & David Blake & Guy Coughlan & Marwa Khalaf-Allah, 2011. "A Gravity Model of Mortality Rates for Two Related Populations," North American Actuarial Journal, Taylor & Francis Journals, vol. 15(2), pages 334-356.
    24. Li, Johnny Siu-Hang & Zhou, Rui & Hardy, Mary, 2015. "A step-by-step guide to building two-population stochastic mortality models," Insurance: Mathematics and Economics, Elsevier, vol. 63(C), pages 121-134.
    25. Johnny Li & Mary Hardy, 2011. "Measuring Basis Risk in Longevity Hedges," North American Actuarial Journal, Taylor & Francis Journals, vol. 15(2), pages 177-200.
    26. Andrew J. G. Cairns, 2013. "Robust Hedging of Longevity Risk," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 80(3), pages 621-648, September.
    27. Christiansen, Marcus C. & Spodarev, Evgeny & Unseld, Verena, 2015. "Differences In European Mortality Rates: A Geometric Approach On The Age–Period Plane," ASTIN Bulletin, Cambridge University Press, vol. 45(3), pages 477-502, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kaakaï, Sarah & Labit Hardy, Héloïse & Arnold, Séverine & El Karoui, Nicole, 2019. "How can a cause-of-death reduction be compensated for by the population heterogeneity? A dynamic approach," Insurance: Mathematics and Economics, Elsevier, vol. 89(C), pages 16-37.
    2. Guibert, Quentin & Lopez, Olivier & Piette, Pierrick, 2019. "Forecasting mortality rate improvements with a high-dimensional VAR," Insurance: Mathematics and Economics, Elsevier, vol. 88(C), pages 255-272.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Blake, David & Cairns, Andrew J.G., 2021. "Longevity risk and capital markets: The 2019-20 update," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 395-439.
    2. Blake, David & El Karoui, Nicole & Loisel, Stéphane & MacMinn, Richard, 2018. "Longevity risk and capital markets: The 2015–16 update," Insurance: Mathematics and Economics, Elsevier, vol. 78(C), pages 157-173.
    3. Liu, Yanxin & Li, Johnny Siu-Hang, 2016. "It’s all in the hidden states: A longevity hedging strategy with an explicit measure of population basis risk," Insurance: Mathematics and Economics, Elsevier, vol. 70(C), pages 301-319.
    4. Hunt, Andrew & Blake, David, 2018. "Identifiability, cointegration and the gravity model," Insurance: Mathematics and Economics, Elsevier, vol. 78(C), pages 360-368.
    5. Guibert, Quentin & Lopez, Olivier & Piette, Pierrick, 2019. "Forecasting mortality rate improvements with a high-dimensional VAR," Insurance: Mathematics and Economics, Elsevier, vol. 88(C), pages 255-272.
    6. Selin Ozen & c{S}ule c{S}ahin, 2021. "A Two-Population Mortality Model to Assess Longevity Basis Risk," Papers 2101.06690, arXiv.org.
    7. Selin Özen & Şule Şahin, 2021. "A Two-Population Mortality Model to Assess Longevity Basis Risk," Risks, MDPI, vol. 9(2), pages 1-19, February.
    8. Hunt, Andrew & Blake, David, 2015. "Modelling longevity bonds: Analysing the Swiss Re Kortis bond," Insurance: Mathematics and Economics, Elsevier, vol. 63(C), pages 12-29.
    9. Bravo, Jorge M. & Ayuso, Mercedes & Holzmann, Robert & Palmer, Edward, 2021. "Addressing the life expectancy gap in pension policy," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 200-221.
    10. Jarner, Søren F. & Jallbjørn, Snorre, 2020. "Pitfalls and merits of cointegration-based mortality models," Insurance: Mathematics and Economics, Elsevier, vol. 90(C), pages 80-93.
    11. Liu, Yanxin & Li, Johnny Siu-Hang, 2018. "A strategy for hedging risks associated with period and cohort effects using q-forwards," Insurance: Mathematics and Economics, Elsevier, vol. 78(C), pages 267-285.
    12. Cairns, Andrew J.G., 2011. "Modelling and management of longevity risk: Approximations to survivor functions and dynamic hedging," Insurance: Mathematics and Economics, Elsevier, vol. 49(3), pages 438-453.
    13. Li, Jackie & Haberman, Steven, 2015. "On the effectiveness of natural hedging for insurance companies and pension plans," Insurance: Mathematics and Economics, Elsevier, vol. 61(C), pages 286-297.
    14. Massimiliano Menzietti & Maria Francesca Morabito & Manuela Stranges, 2019. "Mortality Projections for Small Populations: An Application to the Maltese Elderly," Risks, MDPI, vol. 7(2), pages 1-25, March.
    15. Bozikas, Apostolos & Pitselis, Georgios, 2020. "Incorporating crossed classification credibility into the Lee–Carter model for multi-population mortality data," Insurance: Mathematics and Economics, Elsevier, vol. 93(C), pages 353-368.
    16. Uditha Balasooriya & Johnny Siu-Hang Li & Jackie Li, 2020. "The Impact of Model Uncertainty on Index-Based Longevity Hedging and Measurement of Longevity Basis Risk," Risks, MDPI, vol. 8(3), pages 1-27, August.
    17. Tan, Chong It & Li, Jackie & Li, Johnny Siu-Hang & Balasooriya, Uditha, 2014. "Parametric mortality indexes: From index construction to hedging strategies," Insurance: Mathematics and Economics, Elsevier, vol. 59(C), pages 285-299.
    18. Danesi, Ivan Luciano & Haberman, Steven & Millossovich, Pietro, 2015. "Forecasting mortality in subpopulations using Lee–Carter type models: A comparison," Insurance: Mathematics and Economics, Elsevier, vol. 62(C), pages 151-161.
    19. Katrien Antonio & Anastasios Bardoutsos & Wilbert Ouburg, 2015. "Bayesian Poisson log-bilinear models for mortality projections with multiple populations," Working Papers Department of Accountancy, Finance and Insurance (AFI), Leuven 485564, KU Leuven, Faculty of Economics and Business (FEB), Department of Accountancy, Finance and Insurance (AFI), Leuven.
    20. McCarthy, David G. & Wang, Po-Lin, 2021. "Pooling mortality risk in Eurozone state pension liabilities: An application of a Bayesian coherent multi-population cohort-based mortality model," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 459-485.

    More about this item

    Keywords

    Danish mortality data; affluence; CBD-X model; gravity model; multipopulation mortality modelling;
    All these keywords.

    JEL classification:

    • J11 - Labor and Demographic Economics - - Demographic Economics - - - Demographic Trends, Macroeconomic Effects, and Forecasts
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aah:create:2016-14. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: http://www.econ.au.dk/afn/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.