Structural equation modeling for decomposing rank-dependent indicators of socioeconomic inequality of health: an empirical study.

Guido Erreygers,Roselinde Kessels

doi:10.1186/s13561-016-0134-2

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.

Highlights

The dominant approach to the measurement of socioeconomic inequality of health consists of using rankdependent indicators
Compared to indicators of income inequality or health inequality, which measure the degree of inequality within a given univariate distribution of income or health, indicators of socioeconomic inequality of health are bivariate in nature because they measure the degree of correlation between health and socioeconomic status
Data description For comparison the data are the same as those used by Erreygers and Kessels [4]. They come from the 2011 Demographic and Health Survey (DHS) of Ethiopia and are confined to children under the age of five

Summary

Introduction

The dominant approach to the measurement of socioeconomic inequality of health consists of using rankdependent indicators. They are called rank-dependent because they can be expressed as weighted averages of individual health levels, with the weights determined by the ranks of individuals in the socioeconomic distribution. The most salient of these decompositions is based on the bivariate multiple regression model that explains health and socioeconomic status simultaneously. This decomposition captures the direct contributions of the explanatory variables in the regressions, and their combined or correlated contributions

Methods

Results

Conclusion