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具有常数交叉乘积比的动态多状态模型:在贫困状况中的应用。

Dynamic Multistate Models With Constant Cross-Product Ratios: Applications to Poverty Status.

机构信息

Department of Sociology, Pennsylvania State University, University Park, State College, PA, 16802, USA.

出版信息

Demography. 2020 Apr;57(2):779-797. doi: 10.1007/s13524-020-00865-9.

DOI:10.1007/s13524-020-00865-9
PMID:32212098
Abstract

Cross-product ratios (αs), which are structurally analogous to odds ratios, are statistically sound and demographically meaningful measures. Assuming constant cross-product ratios in the elements of a matrix of multistate transition probabilities provides a new basis both for calculating probabilities from minimal data and for modeling populations with changing demographic rates. Constant-α estimation parallels log linear modeling, in which the αs are the fixed interactions, and the main effects are calculated from relevant data. Procedures are presented showing how an N state model's matrix of transition probabilities can be found from the constant αs and (1) the state composition of adjacent populations, (2) (N - 1) known probabilities, (3) (N - 1) known transfer rates, or (4) (2N - 1) known numbers of transfers. The scope and flexibility of constant-α models makes them applicable to a broad range of demographic subjects, including marital/union status, political affiliation, residential status, and labor force status. Here, an application is provided to the important but understudied topic of poverty status. Census data, separately for men and women, provide age-specific numbers of persons in three poverty statuses for the years 2009 and 2014. Using an estimated transition matrix that furnishes a set of cross-product ratios, the constant-α approach allows the calculation of male and female poverty status life tables for the 2009-2014 period. The results describe the time spent in each poverty state and the transitions between states over the entire life course.

摘要

交叉乘积比(αs)在结构上类似于优势比,是一种统计学上合理且具有人口学意义的度量方法。假设矩阵中多状态转移概率元素的交叉乘积比保持不变,这为从最小数据计算概率和为具有变化人口率的人群建模提供了新的基础。常数-α估计类似于对数线性建模,其中αs 是固定交互作用,而主效应是从相关数据中计算得出的。本文介绍了如何从常数αs 和(1)相邻群体的状态组成、(2)(N-1)个已知概率、(3)(N-1)个已知转移率或(4)(2N-1)个已知转移次数,找到 N 状态模型的转移概率矩阵。常数-α模型的范围和灵活性使其适用于广泛的人口学主题,包括婚姻/联盟状况、政治派别、居住状况和劳动力状况。这里提供了一个应用于贫困状况这一重要但研究不足的主题的例子。人口普查数据分别按性别列出了 2009 年和 2014 年三个贫困状况的特定年龄人数。使用提供一组交叉乘积比的估计转移矩阵,常数-α方法允许计算 2009-2014 年期间男性和女性贫困状况生命表。结果描述了在每个贫困状态下花费的时间以及整个生命周期中状态之间的转移。

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