Abstract
In this study, we analyse a large sample of Egyptian social pension data which covers, by law, the policyholder’s spouse, children, parents and siblings. This data set uniquely enables the study and comparison of pairwise dependence between multiple familial relationships beyond the well-known husband and wife case. Applying Bayesian Markov Chain Monte Carlo (MCMC) estimation techniques with the two-step inference functions for margins (IFM) method, we model dependence between lifetimes in spousal, parent–child and child–parent relationships, using copulas to capture the strength of association. Dependence is observed to be strongest in child–parent relationships and, in comparison to the high-income countries of data sets previously studied, of lesser significance in the husband and wife case, often referred to as broken-heart syndrome. Given the traditional use of UK mortality tables in the modelling of mortality in Egypt, the findings of this paper will help to inform appropriate mortality assumptions specific to the unique structure of the Egyptian scheme.
Funder
Engineering and Physical Sciences Research Council and Economic and Social Research Council
Subject
Strategy and Management,Economics, Econometrics and Finance (miscellaneous),Accounting
Reference107 articles.
1. Efficient Bayesian inference for stochastic time-varying copula models;Almeida;Computational Statistics & Data Analysis,2012
2. Bayesian Poisson log-bilinear models for mortality projections with multiple populations;Antonio;European Actuarial Journal,2015
3. Joint and survivor annuity valuation with a bivariate reinforced urn process;Arias;Insurance: Mathematics and Economics,2021
4. Bayesian inference of survival probabilities, under stochastic ordering constraints;Arjas;Journal of the American Statistical Association,1996
5. Time-varying joint distribution through copulas;Ausin;Computational Statistics & Data Analysis,2010