使用多阶段聚类方法刻画不同国家和时间的死亡率动态

中文摘要 AI 辅助

跨国家的死亡率动态比较分析长期以来塑造了我们对死亡率不平等以及随时间推移的趋同与分化模式的理解。然而，现有的大多数研究要么关注单一时间点上各国之间的死亡率差异，要么关注国家轨迹，而没有同时考虑这两个因素。在本文中，我们通过引入一个多阶段聚类框架来弥补这一空白，该框架应用于1960-2019年期间来自《世界人口展望》数据的94个国家，并按性别进行分析。具体而言，我们使用一个随时间变化的beta潜变量模型对生命表中的死亡概率进行建模，并识别出一小组“死亡率状态”，这些状态刻画了每个国家在任何给定时间点的死亡率模式。然后，我们根据国家的死亡率状态序列对其进行聚类。我们的结果揭示了各国在死亡率状态之间转变的时间和速度上存在显著的异质性，包括欧洲持续的东-西分化，以及与年轻成年男性死亡率升高相关的独特的拉丁美洲模式。直到1990年代，两性的跨国不平等都在加剧，之后趋于平稳并随后下降。我们提出的多阶段聚类框架提供了对死亡率动态的可解释的、明确时间维度的描述，该描述同时捕捉了国家内部的变化和国家之间的差异，并且可以应用于其他涉及分类或离散化概况轨迹的研究场景。

英文摘要

Comparative analyses of mortality dynamics across countries have long shaped our understanding of mortality inequalities and patterns of divergence and convergence over time. However, most existing studies focus on either mortality differences across countries at a single point in time or on country trajectories, without considering both factors simultaneously. In this paper, we address this gap by introducing a multi-stage clustering framework, applied to 94 countries by sex over the period 1960-2019 using data from the World Population Prospects. Specifically, we model life-table probabilities of death using a time-dependent beta latent variable model and identify a small set of ``mortality states'' that characterise the mortality pattern of each country at any given point in time. We then cluster countries based on their sequences of mortality states. Our results reveal substantial heterogeneity in the timing and pace of transitions between mortality states, including a persistent East-West divide in Europe and distinctive Latin American patterns associated with elevated young-adult male mortality. Cross-country inequality increased for both sexes until the 1990s, before plateauing and subsequently declining. Our proposed multi-stage clustering framework provides an interpretable, time-explicit description of mortality dynamics that jointly captures within-country change and between-country differences, and can be applied to other settings where trajectories of categorical or discretised profiles are of interest.

发表机构

• Max Planck Institute for Demographic Research（马克斯·普朗克人口研究所）

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