Health and Inequality: Volume 21
Table of contents(25 chapters)
Health and Inequality
List of Contributors
What change in the distribution of a population’s health preserves the level of inequality? The answer to this analogous question in the context of income inequality lies somewhere between a uniform and a proportional change. These polar positions represent the absolute and relative inequality equivalence criterion (IEC), respectively. A bounded health variable may be presented in terms of both health attainments and shortfalls. As a distributional change cannot simultaneously be proportional to attainments and to shortfalls, relative inequality measures may rank populations differently from the two perspectives. In contrast to the literature that stresses the importance of measuring inequality in attainments and shortfalls consistently using an absolute IEC, this chapter formalizes a new compromise concept for a bounded variable by explicitly considering the two relative IECs, defined with respect to attainments and shortfalls, to represent the polar cases of defensible positions.
We use a surplus-sharing approach to provide new insights on commonly used inequality indices by evaluating the underpinning IECs in terms of how infinitesimal surpluses of health must be successively distributed to preserve the level of inequality. We derive a one-parameter IEC that, unlike those implicit in commonly used indices, assigns constant weights to the polar cases independent of the health distribution.
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.
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.
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.
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.
Equity in Health and Equivalent Incomes
We compare two approaches to measuring inequity in the health distribution. The first is the concentration index. The second is the calculation of the inequality in an overall measure of individual well-being, capturing both the income and health dimensions. We introduce the concept of equivalent income as a measure of well-being that respects preferences with respect to the trade-off between income and health, but is not subjectively welfarist since it does not rely on the direct measurement of happiness. Using data from a representative survey in France, we show that equivalent incomes can be measured using a contingent valuation method. We present counterfactual simulations to illustrate the different perspectives of the approaches with respect to distributive justice.
Most politicians and ethical observers are not interested in pure health inequalities, as they want to distinguish between different causes of health differences. Measures of “unfair” inequality – direct unfairness and the fairness gap, but also the popular standardized concentration index (CI) – therefore neutralize the effects of what are considered to be “legitimate” causes of inequality. This neutralization is performed by putting a subset of the explanatory variables at reference values, for example, their means. We analyze how the inequality ranking of different policies depends on the specific choice of reference values. We show with mortality data from the Netherlands that the problem is empirically relevant and we suggest a statistical method for fixing the reference values.
Longitudinal data are required to characterise and measure the dynamics of income-related health inequalities (IRHI). This chapter develops a framework to evaluate the impact of population changes on the level of cross-sectional IRHI over time and thereby provides further insight into how health inequalities develop or perpetuate themselves in a society. The approach is illustrated by an empirical analysis of the increase in IRHI in Great Britain between 1999 and 2004 using the British Household Panel Survey. The results imply that levels of IRHI would have been even higher in 2004 but for the entry of youths into the adult population and deaths, with these natural processes of population turnover serving to partially mask the increase in IRHI among the resident adult population over the five-year period. We conclude that a failure to take demographic changes into account may lead to erroneous conclusions on the effectiveness of policies designed to tackle health inequalities.
In this chapter we explore different ways to obtain decompositions of rank-dependent indices of socioeconomic inequality of health, such as the Concentration Index. Our focus is on the regression-based type of decomposition. Depending on whether the regression explains the health variable, or the socioeconomic variable, or both, a different decomposition formula is generated. We illustrate the differences using data from the Ethiopia 2011 Demographic and Health Survey (DHS).
We explore what health-capital theory has to offer in terms of informing and directing research into health inequality. We argue that economic theory can help in identifying mechanisms through which specific socioeconomic indicators and health interact. Our reading of the literature, and our own work, leads us to conclude that non-degenerate versions of the Grossman (1972a, 1972b) model and its extensions can explain many salient stylized facts on health inequalities. Yet, further development is required in at least two directions. First, a childhood phase needs to be incorporated, in recognition of the importance of childhood endowments and investments in the determination of later-life socioeconomic and health outcomes. Second, a unified theory of joint investment in skill (or human) capital and in health capital could provide a basis for a theory of the relationship between education and health.
In this chapter, I review recent evidence on the developmental origins of health inequality. I discuss the origins of the education-health gradient, the long-term costs caused by early life adversity, and how early life experiences affect the biology of the body. Additionally, I provide complementary evidence on enrichment interventions which can at least partially compensate for these gaps. I highlight emerging lines of scientific inquiry which are likely to have a significant impact on the field. I argue that, while the evidence that early life conditions have long-term effects is now uncontroversial, the literature needs to be expanded both in a theoretical and empirical direction. On the one hand, a model linking early life origins to ageing needs to be developed; on the other hand, a better understanding of the mechanisms – both biological and socioeconomic – is required, in order to design more effective interventions.
Health is distributed unequally by occupation. Workers on a lower rung of the occupational ladder report worse health, have a higher probability of disability and die earlier than workers higher up the occupational hierarchy. Using a theoretical framework that unveils some of the potential mechanisms underlying these disparities, three core insights emerge: (i) there is selection into occupation on the basis of initial wealth, education and health, (ii) there will be behavioural responses to adverse working conditions, which can have compensating or reinforcing effects on health and (iii) workplace conditions increase health inequalities if workers with initially low socio-economic status choose harmful occupations and don’t offset detrimental health effects. We provide empirical illustrations of these insights using data for the Netherlands and assess the evidence available in the economics literature.
This chapter aims to quantify and compare inequalities of opportunity in health across European countries considering two alternative normative ways of treating the correlation between effort, as measured by lifestyles, and circumstances, as measured by parental and childhood characteristics, championed by Brian Barry and John Roemer. This study relies on regression analysis and proposes several measures of inequality of opportunity. Data from the Retrospective Survey of SHARELIFE, which focuses on life histories of European people aged 50 and over, are used.
In Europe at the whole, inequalities of opportunity stand for almost 50% of the health inequality due to circumstances and efforts in Barry scenario and 57.5% in Roemer scenario. The comparison of the magnitude of inequalities of opportunity in health across European countries shows considerable inequalities in Austria, France, Spain and Germany, whereas Sweden, Poland, Belgium, the Netherlands and Switzerland present the lowest inequalities of opportunity. The normative principle on the way to treat the correlation between circumstances and efforts makes little difference in Spain, Austria, Greece, France, Czech Republic, Sweden and Switzerland, whereas it would matter the most in Belgium, the Netherlands, Italy, Germany, Poland and Denmark.
In most countries, inequalities of opportunity in health are mainly driven by social background affecting adult health directly, and so would require policies compensating for poorer initial conditions. On the other hand, our results suggest a strong social and family determinism of lifestyles in Belgium, the Netherlands, Italy, Germany, Poland and Denmark, which emphasises the importance of inequalities of opportunity in health within those countries and calls for targeted prevention policies.
The chapter suggests two methodologies to measure inequality of opportunity in health in Israel, an ex-ante and an ex-post approach. In both cases, following the strategy recently suggested by Trannoy, Tubeuf, Jusot, and Devaux (2010), the chapter starts by introducing the production function of health, taking into account circumstances (the father’s years of education, his country of birth, the religion of the individual, his or her country of birth, age and gender) as well as effort variables (the level of education of the individual, his or her occupation and a variable describing his or her smoking habits).
The chapter also suggests then a decomposition of the overall health inequality into a legitimate and an illegitimate component, using the mean logarithmic deviation as inequality index, such a breakdown being applied to both the ex-ante and the ex-post approaches to equality of opportunity.
Prenatal exposure to adverse conditions is known to affect health throughout the life span. It has also been shown that health is unevenly distributed at advanced ages. This chapter investigates whether health inequalities at old age may be partially caused by prenatal circumstances. We use a sample of people aged 71–91 from eight European countries and assess how shocks in GDP that occurred while the respondents were still in utero affect four important dimensions of later-life health: cognition, depression, functional limitations, and grip strength. We find that early-life macro-economic circumstances do not affect health at advanced ages, nor do they affect inequalities in health. In additional analyses, we show that the least healthy people may not enter our sample as the probability of dying before reaching age 71 is high, and mortality rates among those who were prenatally exposed to adverse GDP shocks are higher. We conclude that selective mortality may mask effects of early-life circumstances on health and health inequality at old age.
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.
There is an on-going debate as to whether health is negatively affected by economic inequality. Still, we have limited knowledge of the mechanisms relating inequality to individual health and very little evidence comes from less-developed economies. We use individual and multi-level data from Zambia on child nutritional health to test three hypotheses consistent with a negative correlation between income inequality and population health: the absolute income hypothesis (AIH), the relative income hypothesis (RIH) and the income inequality hypothesis (IIH). The results confirm that absolute income positively affects health. For the RIH we find sensitivity to the reference group used. Most interestingly, we find higher income inequality to robustly associate with better child health. The same pattern appears in a cross country regression. To explain the conflicting results in the literature we suggest examining potential mediators such as generosity, food sharing, trust and purchasing power.
Equality of opportunity (EOp) for health is defined and advocated as the right conceptualization of equity in the allocation of health care resources. EOp is contrasted with the traditional view that equity consists in “horizontal equity,” a state in which all persons in a society with similar health needs receive similar amounts of medical resources. We argue the horizontal equity is neither sufficient nor necessary for distributive justice in this domain. The EOp view holds individuals partially responsible for the quality of lifestyle that they live, in so far as it affects their health, but compensates individuals for the effect on health of circumstances beyond their control, including the effect of circumstances on their lifestyle. EOp generally recommends a distribution of medical resources that is more egalitarian than the utilitarianism recommends, but less egalitarian than the (Rawlsian) maximin view recommends. An example is computed to illustrate the difference between opportunity equalizing and utilitarian health delivery policies.
In this chapter we discuss the cost-effectiveness analysis (CEA) of public health interventions where there are combined, and potentially conflicting, objectives of increasing total population health and reducing unfair health inequalities in the population. Our focus is on identifying appropriate health inequality measures in this context to quantify the impacts of interventions on unfair health inequality and, where necessary, analyse equity-efficiency trade-offs between improving total population health and reducing unfair health inequality. We recognise that this requires a number of important social value judgements to be made, and so prefer measures that facilitate transparency about these social value judgements. We briefly summarise the literature on health inequality and health-related social welfare functions, and conclude that while valuable it is not entirely suitable for our purpose. We borrow instead from the wider literature on economic inequality, highlighting how this translates to a health setting, and identify appropriate measures for CEA. We conclude with a stylised example illustrating how we would apply a battery of dominance rules and social welfare indices to evaluate the health distributions associated with two hypothetical health interventions.
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- Research on Economic Inequality
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- Emerald Publishing Limited
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