Human populations with low survival at advanced ages and postponed fertility reduce long-term growth in high inflation environments

Rahul Mondal, Jose Manuel Aburto, Rebecca Sear, Shripad D Tuljapurkar, Udaya Shankar Mishra, Roberto Salguero-Gomez
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Abstract

Temporal variability in inflation can lead to important fluctuations in the long-term growth rate of human populations via their differential impacts on vital rates like survival and fertility. However, historically, demographic studies have overlooked this time-dependent relationship. Here, we test whether human populations have higher stochastic population growth rates when exposed to lower levels of inflation. We also examine if lower survival rates at older ages (>60 years) and fertility rates at the later reproductive years (>30 years) among populations exposed to higher inflation rates determine their expected lower long-term growth rate compared to those exposed to lower inflation rates. To explore the impact of variability in inflation on vital rates response, we develop a quantitative pipeline with four steps, and parameterise it with high-resolution economic and demographic data across 76 countries from 1971-2021. The four steps are (1) defining treatment groups based on levels of trend inflation (creeping inflation (0-3%), walking inflation (3-10%), galloping inflation (10-50%), and hyperinflation (>50%)) among which the stochastic population growth rates will be compared; (2) constructing matrix population models for each environmental state under every treatment. The environmental states for each treatment are defined on the basis of the duration of inflation (e.g., 0, 2, 4, six years or above); (3) estimating the stochastic population growth rate for each treatment by considering a Markovian environment dictated by the long-term frequency (f) and temporal autocorrelation (ρ) of the treatment; and (4) decomposing the differences in the population growth rate between treatments into contributions from environmental variability and vital rate differences between environments to test how vital rates impact on population growth under varying environmental scenarios. In agreement with our hypothesis, we find that the stochastic population growth rate at lower levels of inflation is systematically higher than that at a higher level of inflation at all stationary frequencies and temporal autocorrelation of the inflation environment. Moreover, the disadvantage in survival at older ages (>60 years) and fertility at ages >30 years led to the lower stochastic growth rate among populations exposed to higher level of inflation such as creeping inflation compared to higher level of inflation, such as walking inflation. Our framework explicitly links human population performance and inflation environment by describing nonlinear feedback between inflation, human survival, fertility, population growth, and its age structure. We discuss the potential of our approach to study the life-history strategies and population dynamics of a wide range of drivers of environmental variability.
在高通胀环境下,高龄存活率低和生育率推迟的人口会降低长期增长率
通货膨胀的时间变化会对存活率和生育率等生命率产生不同的影响,从而导致人口长期增长率的重要波动。然而,从历史上看,人口研究忽视了这种时间依赖关系。在此,我们检验了人类是否会在较低通胀水平下获得更高的随机人口增长率。我们还研究了与那些暴露在较低通货膨胀率下的人口相比,暴露在较高通货膨胀率下的人口在较高年龄段(60 岁)的存活率和在较晚生育年龄段(30 岁)的生育率较低是否决定了其预期的较低长期增长率。为了探讨通货膨胀率的变化对生命周期响应的影响,我们开发了一个包括四个步骤的定量管道,并利用 1971-2021 年间 76 个国家的高分辨率经济和人口数据对其进行了参数化。这四个步骤是:(1) 根据趋势通胀水平(爬行通胀(0-3%)、步行通胀(3-10%)、急速通胀(10-50%)和恶性通胀(50%))定义处理组,并在这些处理组中比较随机人口增长率;(2) 为每种处理下的每种环境状态构建矩阵人口模型。每种处理的环境状态是根据通货膨胀的持续时间(如 0、2、4、6 年或以上)来定义的;(3) 通过考虑由处理的长期频率(f)和时间自相关性(ρ)决定的马尔可夫环境,估计每种处理的随机种群增长率;(4) 将处理间种群增长率的差异分解为环境变异性和环境间生命率差异的贡献,以检验不同环境情景下生命率对种群增长的影响。与我们的假设一致,我们发现在所有静态频率和通货膨胀环境的时间自相关性下,较低通货膨胀水平下的随机种群增长率系统性地高于较高通货膨胀水平下的随机种群增长率。此外,老年(60 岁)的存活率和 30 岁的生育率的劣势导致暴露在较高通胀水平(如爬行通胀)下的人口随机增长率低于暴露在较高通胀水平(如步行通胀)下的人口随机增长率。我们的框架通过描述通货膨胀、人类生存、生育率、人口增长及其年龄结构之间的非线性反馈,明确地将人类人口表现与通货膨胀环境联系起来。我们讨论了我们的方法在研究环境变异的各种驱动因素的生命史策略和人口动态方面的潜力。
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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