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首页> 外文期刊>Theoretical Population Biology >The inverse relationship between life expectancy-induced changes in the old-age dependency ratio and the prospective old-age dependency ratio
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The inverse relationship between life expectancy-induced changes in the old-age dependency ratio and the prospective old-age dependency ratio

机译:寿命寿命诱导的旧时依赖比的变化与前瞻性养老率之间的反向关系

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摘要

Unlike other biological populations, the human population is experiencing long-run increases in life expectancy. Those lead to changes in age compositions not typical for other biological populations. Sanderson and Scherbov (2015a) demonstrated that, in many countries in Europe, faster increases in life expectancy lead to faster population aging when measured using the old-age dependency ratio and to slower population aging when measured using the prospective old-age dependency ratio that employs a dynamic old-age threshold. We examine this finding analytically and with simulations. We use an analytic decomposition of changes in mortality schedules into shift and compression processes. We show that shifts and compressions of mortality schedules push the two old-age dependency ratios in opposite directions. Our formal results are supported by simulations that show a positive effect of a mortality shift on the old-age dependency ratio and a negative effect of it on the prospective old-age dependency ratio. The effects are of opposite sign for a mortality compression. Our formal and simulation results generalize observed European trends and suggest that the inverse relationship between life expectancy and prospective old-age dependency would be observed more generally. (C) 2018 The Authors. Published by Elsevier Inc.
机译:与其他生物群体不同,人口在预期寿命中经历长期的增加。那些导致年龄组成的变化对其他生物群体不典型。 Sanderson和Scherbov(2015A)展示,在欧洲的许多国家,预期寿命的速度更快地导致使用养老阶依赖率测量时更快的人口老龄化,并且使用预期养老依赖率比测量时较慢的人口老化采用动态的旧年龄阈值。我们在分析和模拟中检查了这一发现。我们使用分析分解死亡时间表的变化进入换档和压缩过程。我们表明,死亡率调度的换档和压缩将两个养老龄阶依赖比在相反的方向上推动。我们的正式结果得到了模拟的支持,表现出对养老阶依赖率的死亡率转变的积极影响,并在预期养老比上的负面影响。这种效果是对死亡率压缩的相反标志。我们的正规和模拟结果概括了欧洲趋势,并建议更普遍地观察到预期寿命与预期养老阶段之间的反向关系。 (c)2018作者。 elsevier公司发布

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