On absolute socioeconomic health inequality comparisons.

This paper introduces a new graphical tool: the mean deviation concentration curve. Using a unified approach, we derive the associated dominance conditions that identify robust rankings of absolute socioeconomic health inequality for all indices obeying Bleichrodt and van Doorslaer's (J Health Econ 25:945-957, 2006) principle of income-related health transfer. We also derive dominance conditions that are compatible with other transfer principles available in the literature. To make the identification of all robust orderings implementable using survey data, we discuss statistical inference for these dominance tests. To illustrate the empirical relevance of the proposed approach, we compare joint distributions of income and health-related behavior in the United States.

A framework for testing the equality between the health concentration curve and the 45-degree line.

The health concentration curve is the standard graphical tool to depict socioeconomic health inequality in the literature on health inequality. This paper shows that testing for the absence of socioeconomic health inequality is equivalent to testing if the conditional expectation of health on income is a constant function that is equal to average health status. In consequence, any test for parametric specification of a regression function can be used to test for the absence of socioeconomic health inequality (subject to regularity conditions). Furthermore, this paper illustrates how to test for this equality using a test for parametric regression functional form and applies it to health-related behaviors from the National Health Survey 2014.

Income-related health transfers principles and orderings of joint distributions of income and health.

The objective of this article is to provide the analyst with the necessary tools that allow for a robust ordering of joint distributions of health and income. We contribute to the literature on the measurement and inference of socioeconomic health inequality in three distinct but complementary ways. First, we provide a formalization of the socioeconomic health inequality-specific ethical principle introduced by Erreygers et al. (2012) . Second, we propose new graphical tools and dominance tests for the identification of robust orderings of joint distributions of income and health associated with this new ethical principle. Finally, based on both pro-poor and pro-extreme ranks ethical principles we address a very important aspect of dominance literature: the inference. To illustrate the empirical relevance of the proposed approach, we compare joint distributions of income and a health-related behavior in the United States in 1997 and 2014.

Robust rankings of socioeconomic health inequality using a categorical variable.

When assessing socioeconomic health inequalities, researchers often draw upon measures of income inequality that were developed for ratio scale variables. As a result, the use of categorical data (such as self-reported health status) produces rankings that may be arbitrary and contingent to the numerical scale adopted. In this paper, we develop a method that overcomes this issue by providing conditions for which these rankings are invariant to the numerical scale chosen by the researcher. In doing so, we draw on the insight provided by Allison and Foster (2004) and extend their method to the dimension of socioeconomic inequality by exploiting the properties of rank-dependent indices such as Wagstaff (2002) achievement and extended concentration indices. We also provide an empirical illustration using the National Institute of Health Survey 2012.

Avoiding blindness to health status in health achievement and health inequality measurement.

The concentration index, being focused on the socioeconomic dimension of health inequality and overlooking aversion to pure health inequality, can produce ethically contestable rankings of health distributions. A health transfer from a sicker but richer individual to healthier but poorer individual will decrease the concentration index. This paper presents a new class of health inequality indices that avoid this limitation by trading off socioeconomic-related health inequality against pure health inequality.

Measuring socioeconomic health inequalities in presence of multiple categorical information.

While many of the measurement approaches in health inequality measurement assume the existence of a ratio-scale variable, most of the health information available in population surveys is given in the form of categorical variables. Therefore, the well-known inequality indices may not always be readily applicable to measure health inequality as it may result in the arbitrariness of the health concentration index's value. In this paper, we address this problem by changing the dimension in which the categorical information is used. We therefore exploit the multi-dimensionality of this information, define a new ratio-scale health status variable and develop positional stochastic dominance conditions that can be implemented in a context of categorical variables. We also propose a parametric class of population health and socioeconomic health inequality indices. Finally we provide a twofold empirical illustration using the Joint Canada/United States Surveys of Health 2004 and the National Health Interview Survey 2010.