ArticleHealth economics review2016
Structural equation modeling for decomposing rank-dependent indicators of socioeconomic inequality of health: an empirical study.
Article in Health economics review, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Understanding Period Poverty: Socio-Economic Inequalities in Menstrual Hygiene Management in Eight Low- and Middle-Income Countries.International journal of environmental research and public health · 2021Article
- Anatomy of provincial level inequality in maternal mortality in China during 2004-2016: a new decomposition analysis.BMC public health · 2020Article
- Inequalities in Rotavirus Vaccine Uptake in Ethiopia: A Decomposition Analysis.International journal of environmental research and public health · 2020Article
- The historical roots and seminal research on health equity: a referenced publication year spectroscopy (RPYS) analysis.International journal for equity in health · 2019Article
- Is socioeconomic inequality in postnatal depression an early-life root of disadvantage for children?The European journal of health economics : HEPAC : health economics in prevention and care · 2019Article
- Lifestyle and socio-economic inequalities in diabetes prevalence in South Africa: A decomposition analysis.PloS one · 2019Article
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Abstract
We present a flexible structural equation modeling (SEM) framework for the regression-based decomposition of rank-dependent indicators of socioeconomic inequality of health and compare it with simple ordinary least squares (OLS) regression. The SEM framework forms the basis for a proper use of the most prominent one- and two-dimensional decompositions and provides an argument for using the bivariate multiple regression model for two-dimensional decomposition. Within the SEM framework, the two-dimensional decomposition integrates the feedback mechanism between health and socioeconomic status and allows for different sets of determinants of these variables. We illustrate the SEM approach and its outperformance of OLS using data from the 2011 Ethiopian Demographic and Health Survey.
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