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Health outcomes are often described according to two dimensions: quality of life and quantity of life. We analyze the measurement of inequality of health distributions referring to these two dimensions. Our analysis relies on a novel treatment of the quality-of-life dimension, which might not have a standard mathematical structure. We single out two families of (absolute and relative) multidimensional health inequality indices, inspired by the classical normative approach to income inequality measurement. We also discuss how to extend the analysis to deal with the related problem of health deprivation measurement in this setting.

A number of multidimensional poverty measures that respect the ordinal nature of dimensions have recently been proposed within the counting approach framework. Besides ensuring a reduction in poverty, however, it is important to monitor distributional changes to ensure that poverty reduction has been inclusive in reaching the poorest. Distributional issues are typically captured by adjusting a poverty measure to be sensitive to inequality among the poor. This approach, however, has certain practical and conceptual limitations. It conflicts, for example, with some policy-relevant measurement features, such as the ability to decompose a measure into dimensions post-identification and does not create an appropriate framework for assessing disparity in poverty across population subgroups. In this chapter, we propose and justify the use of a separate decomposable inequality measure – a positive multiple of “variance” – to capture the distribution of deprivations among the poor and to assess disparity in poverty across population subgroups. We demonstrate the applicability of our approach through two contrasting inter-temporal illustrations using Demographic Health Survey data sets for Haiti and India.

Recent decades have witnessed a rising interest in the measurement of inequality from a multidimensional perspective. This literature has however remained largely theoretical. This chapter presents an empirical application of a recent methodology and in doing so offers practical insights on how multidimensional inequality can be measured over two attributes (wealth and health) in the developing country context. Following Abul Naga and Geoffard (2006), a methodological framework allowing the decomposition of multidimensional inequality into two univariate Atkinson–Kolm–Sen equality indices and a third term measuring the association between the attributes is implemented. The methodology is then illustrated using data from the World Health Surveys 2002–2003. Specifically, this study presents the first comparative analysis on multidimensional inequality for a set of Middle East and North African (MENA) countries. Results reveal that the multidimensional (in-)equality indices tend to mimic the (in-)equality ordering of the wealth distributions as the latter are always less equally distributed than health. An empirical conclusion that emerges is that reducing the correlation between the attributes may help to reduce overall welfare inequality, specifically when socioeconomic inequality in health is pro-poor. The finding that the correlation between attributes has a significant contribution in the quantification of inequality has important policy implications since it reveals that it is not only wealth and health inequalities per se that matter in the measurement of welfare inequality but also the associations between them.

By construction, income inequality measures employed in well-being analysis presume all individual differences to be deleterious to the social good. Yet some differences, for example, those acceptable to all and necessary for optimal resource allocation in producing that well-being, are demonstrably beneficial. Measured inequality is an amalgam of both deleterious or ‘Bad’ and beneficial or ‘Good’ differences, and from both policy and well-being measurement perspectives, distinguishing between types with measures fit for purpose makes sense, especially if the types are taking different paths. Here, as an exemplar, the distinction is explored in considering the progress of human resource, gender, and immigrant status-based personal income differences in twenty-first-century Canada. Categorising human resource-based differences as efficiency promoting ‘Good’ inequalities and gender and immigrant status-based differences as discriminatory and ‘Bad’ reveals that, under all proposed measures, while aggregate and ‘Good’ inequality grew over the sample period, ‘Bad’ inequality diminished, reinforcing the case for inequality measures that are fit for purpose.

Much of the theoretical literature on inequality assumes that the equalisand is a cardinal variable like income or wealth. However, health status is generally measured as a categorical variable expressing a qualitative order. Traditional solutions involve reclassifying the variable by means of qualitative models and relying on inequality measures that are mean independent. We argue that the way status is conceptualised has important theoretical implications for measurement as well as for policy analysis. We also bring to the data a recently proposed approach to measuring self-reported health inequality that meets both rigorous and practical considerations. We draw upon the World Health Survey data to examine alternative pragmatic methods for making health-inequality comparisons. Findings suggest significant differences in health-inequality measurement and that regional and country patterns of inequality orderings do not coincide with any reasonable categorisation of countries by health system organisation.

When health is measured by a bounded variable, differences in health can be presented as levels of attainment or shortfall. Measurement of heath inequality then usually involves the choice of either the attainment or the shortfall distribution, and this choice may affect comparisons of inequality across populations. A number of indices have been introduced to overcome this problem. This chapter proposes a framework in which attainment and shortfall distributions can be jointly analyzed. Joint distributions of attainments and shortfalls are defined from points of view consistent with concerns for relative, absolute or intermediate inequality. Inequality measures invariant according to the corresponding ethical criterion are then applied. A dominance criterion that guarantees unanimous rankings of the joint distributions is also proposed.

Traditional indices of bi-dimensional inequality and polarization were developed for cardinal variables and cannot be used to quantify dispersion in ordinal measures of socioeconomic status and health. This chapter develops two approaches to the measurement of inequality and bi-polarization using only ordinal information. An empirical illustration is given for 24 European Union countries in 2004–2006 and 2011. Results suggest that inequalities and bi-polarization in income and health are especially large in Estonia and Portugal, and that inequalities have significantly increased in recent years in Austria, Belgium, Finland, Germany, and the Netherlands, whereas bi-polarization significantly decreased in France, Portugal, and the United Kingdom.

Purpose – We propose applying Reardon's approach to the measurement of ordinal segregation to the study of inequality in life chances in the case of ordinal variables. We also propose additional measures of inequality in life chances in such a case.

Methodology – We state the desirable properties of measures of inequality in life chances when the variable under study is ordinal and check which properties are fulfilled by the various indices examined in this chapter.

Findings – All the indices defined in this chapter seem suitable for the analysis of inequality in life chances with ordinal variables but we found some trade-off between indices fulfilling the population composition invariance and those fulfilling the group replication invariance.

Originality – Besides extending the indices suggested by Reardon to the study of inequality of life chances, we propose, to analyze this issue, two additional sets of indices based on the notion of distributional dissimilarity.

This paper introduces a unit-consistent Lorenz dominance criterion that allows ranking income distributions according to centrist measures à la Seidl and Pfingsten (1997). In doing so, it defines α-Lorenz curves that generalize the absolute Lorenz curve. These curves allow implementing unanimous rankings for a broad set of centrist inequality notions, whereas they become closer and closer to the absolute curve when α approaches equity. In addition, this paper provides an empirical illustration of these tools using Australian income data. The results suggest that despite the reduction of relative inequality for Australian-born people between 1999 and 2003, their inequality increased for most centrist value judgments.