Forecasting the old-age dependency ratio to determine a sustainable pension age
IF 0.8
4区 数学
Q3 STATISTICS & PROBABILITY
引用次数: 6
Abstract
We forecast the old-age dependency ratio for Australia under various pension age proposals, and estimate a pension age scheme that will provide a stable old-age dependency ratio at a specified level. Our approach involves a stochastic population forecasting method based on coherent functional data models for mortality, fertility and net migration, which we use to simulate the future age-structure of the population. Our results suggest that the Australian pension age should be increased to 68 by 2030, 69 by 2036 and 70 by 2050, in order to maintain the old-age dependency ratio at 23%, just above the 2018 level. Our general approach can easily be extended to other target levels of the old-age dependency ratio and to other countries.
预测老年抚养比，确定可持续的领取养老金年龄
我们预测了澳大利亚在不同养老金年龄建议下的老年抚养比，并估计了一种养老金年龄计划，该计划将在特定水平上提供稳定的老年抚养比。我们的方法涉及一种随机人口预测方法，该方法基于死亡率、生育率和净迁移的连贯功能数据模型，我们使用该模型来模拟人口的未来年龄结构。我们的研究结果表明，到2030年，澳大利亚的养老金年龄应该提高到68岁，到2036年提高到69岁，到2050年提高到70岁，以使老年抚养比保持在23%，略高于2018年的水平。我们的一般方法可以很容易地推广到老年抚养比率的其他目标水平和其他国家。
本文章由计算机程序翻译，如有差异，请以英文原文为准。
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来源期刊
CiteScore
1.30
自引率
9.10%
发文量
31
审稿时长
>12 weeks
期刊介绍：
The Australian & New Zealand Journal of Statistics is an international journal managed jointly by the Statistical Society of Australia and the New Zealand Statistical Association. Its purpose is to report significant and novel contributions in statistics, ranging across articles on statistical theory, methodology, applications and computing. The journal has a particular focus on statistical techniques that can be readily applied to real-world problems, and on application papers with an Australasian emphasis. Outstanding articles submitted to the journal may be selected as Discussion Papers, to be read at a meeting of either the Statistical Society of Australia or the New Zealand Statistical Association.
The main body of the journal is divided into three sections.
The Theory and Methods Section publishes papers containing original contributions to the theory and methodology of statistics, econometrics and probability, and seeks papers motivated by a real problem and which demonstrate the proposed theory or methodology in that situation. There is a strong preference for papers motivated by, and illustrated with, real data.
The Applications Section publishes papers demonstrating applications of statistical techniques to problems faced by users of statistics in the sciences, government and industry. A particular focus is the application of newly developed statistical methodology to real data and the demonstration of better use of established statistical methodology in an area of application. It seeks to aid teachers of statistics by placing statistical methods in context.
The Statistical Computing Section publishes papers containing new algorithms, code snippets, or software descriptions (for open source software only) which enhance the field through the application of computing. Preference is given to papers featuring publically available code and/or data, and to those motivated by statistical methods for practical problems.
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