使用具有协变量的潜在马尔可夫模型评估自我报告的物质幸福感

IF 1.2 4区 社会学 Q4 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH Longitudinal and Life Course Studies Pub Date : 2023-03-10 DOI:10.1332/175795921X16719290621875
Ewa Genge
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引用次数: 2

摘要

家庭的经济满意度是驱动主观幸福感的最重要因素之一。然而,波兰的自我报告财务状况接近最低,在28个欧盟成员国中排名第22。本文调查了波兰家庭行为模式的确定问题,并展示了基于八波波兰家庭面板数据的财务状况主观评估的演变。分析是在潜在马尔可夫(LM)模型的基础上进行的,该模型考虑了影响潜在过程参数的社会经济特征。我们比较了不同类型的LM模型,考虑到:(1)不同数量的潜在结构;(2) 不同类型的潜在过程约束;(3) 社会经济背景特征;以及(4)调查权重(在大多数实证分析中被排除在外)。最终的模型确定了三种潜在状态,指定了15年内常见的初始和过渡概率,因此,使我们能够更好地描述可能改变立场的家庭,特别是报告财务状况恶化的家庭。为了显示自我报告财务状况的主要方向,我们根据所选LM模型的后验概率最大化算法,为以所选社会经济特征为特征的受访者提供了预测路径。
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An evaluation of self-reported material well-being using latent Markov models with covariates.

A household's financial satisfaction is one of the most significant factors driving subjective well-being. However, Poland ranks close to the lowest position, 22nd out of the 28 EU members, in self-reported financial status. The paper investigates the problem of determining patterns of Polish households' behaviour and shows the evolution of the subjective assessment of financial situation based on the eight waves of the Polish Household panel data. The analysis is carried out on the basis of latent Markov (LM) models, which allow for socio-economic features affecting the parameters of the latent process. We compare different types of LM models considering: (1) different numbers of latent structures; (2) different types of the latent process constraints; (3) socio-economic background characteristics; and (4) survey weights (being excluded in most of the empirical analyses). The final model identifies three latent states, specifies common initial and transition probabilities over a 15-year period and, as a result, enables us to better characterise the families likely to change their position, especially families reporting worsening in their financial situation. To show the main direction of self-reporting financial condition, we present the predicted path for respondents characterised by the selected socio-economic features, relying on algorithm maximising posterior probabilities of the selected LM model.

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来源期刊
CiteScore
2.50
自引率
11.10%
发文量
43
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