Measuring Socioeconomic Health Inequalities
Friday, Jan. 6, 2017 5:30 PM – 7:15 PM
- Chair: Paul Makdissi, University of Ottawa
Entropy-Based Measures of Multidimensional Health Inequality
AbstractWe propose a new measure of multidimensional health inequality based on a well-known class of entropy-based inequality measures. We aggregate a set of health attributes into a measure of overall health by minimizing the relative entropic distance between the multivariate distribution of attributes and the distribution of the summary measure. We calculate both general inequality and income-based inequality using generalized entropy, which has many desirable properties in the context of multidimensional inequality measurement. This methodology offers many advantages in comparison to the Gini and condition index family of inequality measures, including accounting for the multidimensional nature of health, allowing flexibility in the degree of complementarity between health attributes, utilizing the information from the entire distribution of health and income rather than income or health ranks, and making normative assumptions transparent and intuitive. We demonstrate the contribution of our inequality measure using data from the Health and Retirement Surveys which contains multiple measures of clinical health, self-reported physical and mental health, and measures of income and wealth.
The Dynamics of Income-Related Health Inequality Across the Life Cycle
AbstractThe positive association between health and income often strengthens at younger ages before reaching a middle age peak and then weakens at older ages. However, there is limited evidence about what features of the co-evolution of health and i ncome over the lifecycle gives rise to this profile. This paper uncovers the changing nature of the inter-dependence between income and health over the lifecycle by further developing longitudinal decomposition techniques. We examine changes in income-related health inequality for rolling age groups by sex for Great Britain (1999-2004) and Australia (2001-2006). We find at younger ages health-related income mobility plays the major role with the sick falling behind their peers in terms of income growth. At middle ages income-related morbidity mobility starts to set in with the poor losing health more quickly than the rich but at older ages income-related mortality plays the major role with the poor more likely to die than the rich. Different mechanisms are at play at different ages and therefore when accounting for changes in income-related health inequalities over time it is important to consider these age sub-groups separately.
Measuring Income-Related Health Inequalities With Uncertain Future Health Prospects
AbstractMeasurement of health disparities is a key component for the assessment of health systems. The extent to which disparities in health are systematically associated with income has been proposed as a measure of disparities in realized health outcomes. On average the poor not only have worse health than the rich but also experience a quicker deterioration in their health over time. However, by looking at realized health outcomes and trajectories, we ignore the variation in the health trajectories faced by poor and rich individuals. In particular we do not know whether the quicker expected average deterioration in the health of the poor is due to a homogenous negative expected impact experienced by all poor people or due to a subset of the poor experiencing large negative shocks to their health. Individuals that are averse to uncertainty are likely to place more weight on worse possible future health states. This paper adds to the current literature on the longitudinal measurement of income-related health inequalities by developing methods which incorporate the level of uncertainty in future health prospects into the picture. Using data from the Australian Household, Income and Labour Dynamics in Australia (HILDA) Survey from 2002 until 2013 we find that the poor were not only expected to lose more health than the rich over time but they also faced greater uncertainty around their future health prospects than the rich.
Categorical Health Variables: What Can We Learn About Socioeconomic Health Inequalities?
AbstractWhen 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 problem 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 Alkire and Foster (2004) and extend their method to the dimension of socioeconomic inequality exploiting the properties of rank dependent indices such as Wagstaff (2002) Achievement and Extended Concentration indices and Erreygers, Clark and Van Ourti (2012) Symmetric Socioeconomic Health Inequality indices. We then provide an empirical illustration using the National Institute of Health Survey 2012.
- H0 - General
- I0 - General