IDEAS home Printed from https://ideas.repec.org/a/pab/rmcpee/v16y2013i1p47-67.html
   My bibliography  Save this article

A Comparison between General Population Mortality and Life Tables for Insurance in Mexico under Gender Proportion Inequality || Una comparación entre la mortalidad de la población general y las tablas de vida de los seguros en México ante porcentajes desiguales de género

Author

Listed:
  • Ornelas, Arelly

    (Department of Econometrics, Riskcenter-IREA. Universitat de Barcelona (España))

  • Guillén, Montserrat

    (Department of Econometrics, Riskcenter-IREA. Universitat de Barcelona (España))

Abstract

We model the mortality behavior of the general population in Mexico using data from 1990 to 2009 and compare it to the mortality assumed in the tables used in Mexico for insured lives. We _t a Lee-Carter model, a Renshaw-Haberman model and an Age-Period-Cohort model. The data used are drawn from the Mexican National Institute of Statistics and Geography (INEGI) and the National Population Council (CONAPO). We also fit a Brass-type relational model to compare gaps between general population mortality and the mortality estimates for the insured population used by the National Insurance and Finance Commission in Mexico. As the life tables for insured lives are unisex, i.e. they do not differentiate between men and women, we assume various sex ratios in the mortality tables for insured lives. We compare our results with those obtained for Switzerland and observe very similar outcomes. We emphasize the limitations of the mortality tables used by insurance companies in Mexico. We also discuss the bias incurred when using unisex mortality tables if the proportion of male and female policyholders in an insurance company is not balanced. || Interesados en conocer las diferencias entre la mortalidad general y la de un subgrupo de la población, como son los asegurados en una compañía de seguros, hemos ajustado un modelo relacional Brass-Type. Para ello, en primer lugar, hemos modelado el comportamiento de la mortalidad de la población general de México entre los años 1990 y 2009. Hemos ajustado un modelo Lee-Carter, un modelo Renshaw-Haberman y un modelo edad-período-cohorte. Los datos utilizados proceden del Instituto Nacional de Estadística y Geografía (INEGI) y el Consejo Nacional de Población (CONAPO). Una vez estimadas las tasas de mortalidad se han comparado con la mortalidad asumida por las compañías aseguradoras mexicanas. Estas tasas de mortalidad han sido calculadas por la Comisión Nacional de Seguros y Finanzas de México. Como las tablas de mortalidad del seguro de vida son unisex, es decir, que no distinguen entre hombres y mujeres, hemos creado diferente escenarios modificando el porcentaje de hombres y mujeres en las tablas de mortalidad. Comparamos los parámetros estimados con los parámetros obtenidos en un análisis con la población Suiza y se observan resultados muy similares. Finalmente, hacemos hincapié en las limitaciones de las tablas de mortalidad utilizadas por las compañías de seguros en México y se analiza el sesgo cuando la proporción de los asegurados masculinos y femeninos en una compañía de seguros no está equilibrada.

Suggested Citation

  • Ornelas, Arelly & Guillén, Montserrat, 2013. "A Comparison between General Population Mortality and Life Tables for Insurance in Mexico under Gender Proportion Inequality || Una comparación entre la mortalidad de la población general y las tablas," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 16(1), pages 47-67, December.
  • Handle: RePEc:pab:rmcpee:v:16:y:2013:i:1:p:47-67
    as

    Download full text from publisher

    File URL: http://www.upo.es/RevMetCuant/pdf/vol16/art78.pdf
    Download Restriction: no

    File URL: http://www.upo.es/RevMetCuant/bibtex.php?id=78
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Arthur Renshaw & Steven Haberman, 2003. "Lee–Carter mortality forecasting: a parallel generalized linear modelling approach for England and Wales mortality projections," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 52(1), pages 119-137, January.
    3. Debón, A. & Montes, F. & Puig, F., 2008. "Modelling and forecasting mortality in Spain," European Journal of Operational Research, Elsevier, vol. 189(3), pages 624-637, September.
    4. Renshaw, A.E. & Haberman, S., 2006. "A cohort-based extension to the Lee-Carter model for mortality reduction factors," Insurance: Mathematics and Economics, Elsevier, vol. 38(3), pages 556-570, June.
    5. 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.
    6. Brouhns, Natacha & Denuit, Michel & Vermunt, Jeroen K., 2002. "A Poisson log-bilinear regression approach to the construction of projected lifetables," Insurance: Mathematics and Economics, Elsevier, vol. 31(3), pages 373-393, December.
    7. Pitacco, Ermanno & Denuit, Michel & Haberman, Steven & Olivieri, Annamaria, 2009. "Modelling Longevity Dynamics for Pensions and Annuity Business," OUP Catalogue, Oxford University Press, number 9780199547272.
    8. Guillén, Montserrat, 2012. "Sexless and beautiful data: from quantity to quality," Annals of Actuarial Science, Cambridge University Press, vol. 6(02), pages 231-234, September.
    Full references (including those not matched with items on IDEAS)

    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. Han Lin Shang & Steven Haberman, 2020. "Retiree Mortality Forecasting: A Partial Age-Range or a Full Age-Range Model?," Risks, MDPI, vol. 8(3), pages 1-11, July.
    2. 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.
    3. 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.
    4. Hunt, Andrew & Villegas, Andrés M., 2015. "Robustness and convergence in the Lee–Carter model with cohort effects," Insurance: Mathematics and Economics, Elsevier, vol. 64(C), pages 186-202.
    5. Hatzopoulos, P. & Haberman, S., 2011. "A dynamic parameterization modeling for the age-period-cohort mortality," Insurance: Mathematics and Economics, Elsevier, vol. 49(2), pages 155-174, September.
    6. 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.
    7. Basellini, Ugofilippo & Camarda, Carlo Giovanni & Booth, Heather, 2023. "Thirty years on: A review of the Lee–Carter method for forecasting mortality," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1033-1049.
    8. Man Chung Fung & Gareth W. Peters & Pavel V. Shevchenko, 2016. "A unified approach to mortality modelling using state-space framework: characterisation, identification, estimation and forecasting," Papers 1605.09484, arXiv.org.
    9. Jaap Spreeuw & Iqbal Owadally & Muhammad Kashif, 2022. "Projecting Mortality Rates Using a Markov Chain," Mathematics, MDPI, vol. 10(7), pages 1-18, April.
    10. Rachel WINGENBACH & Jong-Min KIM & Hojin JUNG, 2020. "Living Longer in High Longevity Risk," JODE - Journal of Demographic Economics, Cambridge University Press, vol. 86(1), pages 47-86, March.
    11. Haberman, Steven & Renshaw, Arthur, 2011. "A comparative study of parametric mortality projection models," Insurance: Mathematics and Economics, Elsevier, vol. 48(1), pages 35-55, January.
    12. Li, Hong & De Waegenaere, Anja & Melenberg, Bertrand, 2015. "The choice of sample size for mortality forecasting: A Bayesian learning approach," Insurance: Mathematics and Economics, Elsevier, vol. 63(C), pages 153-168.
    13. David Atance & Ana Debón & Eliseo Navarro, 2020. "A Comparison of Forecasting Mortality Models Using Resampling Methods," Mathematics, MDPI, vol. 8(9), pages 1-21, September.
    14. Jonas Hirz & Uwe Schmock & Pavel V. Shevchenko, 2017. "Actuarial Applications and Estimation of Extended CreditRisk+," Risks, MDPI, vol. 5(2), pages 1-29, March.
    15. Karim Barigou & Stéphane Loisel & Yahia Salhi, 2020. "Parsimonious Predictive Mortality Modeling by Regularization and Cross-Validation with and without Covid-Type Effect," Risks, MDPI, vol. 9(1), pages 1-18, December.
    16. Li, Johnny Siu-Hang, 2010. "Pricing longevity risk with the parametric bootstrap: A maximum entropy approach," Insurance: Mathematics and Economics, Elsevier, vol. 47(2), pages 176-186, October.
    17. Wang, Chou-Wen & Huang, Hong-Chih & Hong, De-Chuan, 2013. "A feasible natural hedging strategy for insurance companies," Insurance: Mathematics and Economics, Elsevier, vol. 52(3), pages 532-541.
    18. Dowd, Kevin & Cairns, Andrew J.G. & Blake, David & Coughlan, Guy D. & Epstein, David & Khalaf-Allah, Marwa, 2010. "Evaluating the goodness of fit of stochastic mortality models," Insurance: Mathematics and Economics, Elsevier, vol. 47(3), pages 255-265, December.
    19. 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.
    20. Susanna Levantesi & Virginia Pizzorusso, 2019. "Application of Machine Learning to Mortality Modeling and Forecasting," Risks, MDPI, vol. 7(1), pages 1-19, February.

    More about this item

    Keywords

    mortality rates; Lee-Carter; longevity dynamics; Brass-type model; insured population; tasas de mortalidad; modelo Lee-Carter; modelo Brass-Type; población asegurada;
    All these keywords.

    JEL classification:

    • I13 - Health, Education, and Welfare - - Health - - - Health Insurance, Public and Private
    • I14 - Health, Education, and Welfare - - Health - - - Health and Inequality
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General

    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:pab:rmcpee:v:16:y:2013:i:1:p:47-67. 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: Publicación Digital - UPO (email available below). General contact details of provider: https://edirc.repec.org/data/dmupoes.html .

    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.