A latent class analysis towards stability and changes in breadwinning patterns among coupled households

IF 0.8 Q4 STATISTICS & PROBABILITY Dependence Modeling Pub Date : 2019-01-01 DOI:10.1515/demo-2019-0012
F. Pennoni, M. Nakai
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引用次数: 4

Abstract

Abstract A latent class model is proposed to examine couples’ breadwinning typologies and explain the wage differentials according to the socio-demographic characteristics of the society with data collected through surveys. We derive an ordinal variable indicating the couple’s income provision-role type and suppose the existence of an underlying discrete latent variable to model the effect of covariates. We use a two-step maximum likelihood inference conducted to account for concomitant variables, informative sampling scheme and missing responses. The weighted log-likelihood is maximised through the Expectation-Maximization algorithm and information criteria are used to develop the model selection. Predictions are made on the basis of the maximum posterior probabilities. Disposing of data collected in Japan over thirty years we compare couples’ breadwinning patterns across time. We provide some evidence of the gender wage-gap and we show that it can be attributed to the fact that, especially in Japan, duties and responsibilities for the child care are supported exclusively by women.
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对夫妇家庭养家糊口模式的稳定性和变化的潜在阶级分析
摘要通过调查收集的数据,提出了一个潜在的阶级模型来检验夫妻养家糊口的类型,并根据社会的社会人口特征解释工资差异。我们导出了一个指示夫妻收入提供角色类型的序数变量,并假设存在一个潜在的离散潜变量来模拟协变量的影响。我们使用两步最大似然推理来解释伴随变量、信息采样方案和缺失响应。通过期望最大化算法使加权对数似然最大化,并使用信息标准来进行模型选择。预测是在最大后验概率的基础上进行的。根据日本30多年来收集的数据,我们比较了不同时期夫妻的养家糊口模式。我们提供了一些关于性别工资差距的证据,我们表明,这可以归因于这样一个事实,特别是在日本,照顾孩子的职责和责任完全由妇女承担。
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来源期刊
Dependence Modeling
Dependence Modeling STATISTICS & PROBABILITY-
CiteScore
1.00
自引率
0.00%
发文量
18
审稿时长
12 weeks
期刊介绍: The journal Dependence Modeling aims at providing a medium for exchanging results and ideas in the area of multivariate dependence modeling. It is an open access fully peer-reviewed journal providing the readers with free, instant, and permanent access to all content worldwide. Dependence Modeling is listed by Web of Science (Emerging Sources Citation Index), Scopus, MathSciNet and Zentralblatt Math. The journal presents different types of articles: -"Research Articles" on fundamental theoretical aspects, as well as on significant applications in science, engineering, economics, finance, insurance and other fields. -"Review Articles" which present the existing literature on the specific topic from new perspectives. -"Interview articles" limited to two papers per year, covering interviews with milestone personalities in the field of Dependence Modeling. The journal topics include (but are not limited to):  -Copula methods -Multivariate distributions -Estimation and goodness-of-fit tests -Measures of association -Quantitative risk management -Risk measures and stochastic orders -Time series -Environmental sciences -Computational methods and software -Extreme-value theory -Limit laws -Mass Transportations
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