澳大利亚死亡率的空间变异性和社会经济因素

M. Sherris, A. Tang
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引用次数: 1

摘要

众所周知,死亡率因地理位置和社会经济因素而异。人口、种族和社会经济死亡因素因地理位置而异。由于相似的社会经济因素和人口特征,距离较近的区域预计死亡率相似。在本文中,使用空间模型评估了澳大利亚死亡率的空间变异性以及解释性风险因素,包括年龄、收入、劳动力参与率和失业率。地理差异是基于统计细分,相似的社会和经济背景的地区。采用分层贝叶斯模型和马尔可夫链蒙特卡罗方法对1996年、2001年和2006年澳大利亚208个统计分区的死亡率进行Logistic回归估计。当可获得的社会经济因素数据有限时,空间模型比非空间模型更能解释按地理位置划分的死亡率变化。解释因素在空间上也各不相同,因此减少了对死亡率空间模型的需求。该模型对寿险公司的定价和风险管理具有指导意义。风险的地理差异可以使用空间模型进行量化,特别是在产生死亡率异质性的风险因素数据有限的情况下。研究发现,就业和劳动力参与、种族背景以及收入是解释澳大利亚不同地理位置死亡率差异的重要因素。最近在英国,基于邮政编码的地理位置已被用于死亡率和长寿风险产品的定价和风险管理。如本文所示,空间地理人口模型在评估死亡风险时应引起保险公司的极大兴趣。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Spatial Variability in Mortality and Socioeconomic Factors for Australian Mortality
Mortality rates are known to vary by geographical location and to depend on socio-economic factors. Demographic, ethnic and socio-economic mortality factors vary by geographical location. Regions that are in closer proximity are expected to have similar mortality because of similar socio-economic factors and demographic characteristics. In this paper the spatial variability of Australian mortality is assessed using a spatial model along with explanatory risk factors including age, income, labour force participation and unemployment rate. Geo- graphical variation is based on statistical subdivisions, areas of similar social and economic backgrounds. Logistic regressions are estimated using an hierarchical Bayes model with Markov Chain Monte Carlo methods for mortality rates in 208 statistical subdivisions in Australia for census years 1996, 2001 and 2006. Spatial models explain mortality variation by geographical location better than non-spatial models when limited data is available for socio-economic factors. Explanatory factors, which also vary spatially, reduce the need for spatial models for mortality. The modeling has implications for pricing and risk management in life insurance companies. Geographical variation in risks can be quantied using spatial models especially if there is limited data for risk factors that gen- erate mortality heterogeneity. Employment and workforce participation, ethnic background as well as income are found to be signicant in explaining mortality variation by geographical location in Australia. Geographical location has been used recently in the UK based on postcode in pricing and risk management of mortality and longevity risk products. As demonstrated in this paper, spatial geodemographic models should be of signicant interest to insurers in assessing mortality risk.
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